Face Template Research Articles

Three-dimensional analysis of facial morphology in nine-year-old children with different unilateral orofacial clefts compared to normative data.

To compare three-dimensional (3D) facial morphology of various unilateral cleft subphenotypes at 9-years of age to normative data using a general face template and automatic landmarking. The secondary objective is to compare facial morphology of 9-year-old children with unilateral fusion to differentiation defects. 3D facial stereophotogrammetric images of 9-year-old unilateral cleft patients were imported into 3DMedX® for processing. All images of patients with a right sided cleft were mirrored. A regionalized general facial template was used for standardization. This template was pre-aligned to each face using five automatically determined landmarks and fitted using MeshMonk. All cleft patients were compared to an age-and gender matched normative face using distance maps and inter-surface distances (mm). Average faces were created for five groups (unilateral cleft lip, alveolus, and/or palate (UCL/A/P), fusion and differentiation defects). The selected regions for the evaluation of facial morphology were: complete face, nose, upper lip, lower lip, chin, forehead, and cheeks. A total of 86 consecutive 3D-stereophotogrammetry images were acquired for examination. No statistically significant differences were observed among the UCL, UCLA, and UCLP groups for the complete face, cheeks, chin, forehead, lower lip, and nose. However, in the upper lip region a significant difference was observed between the UCLP and UCL groups (P = 0.004, CI [-2.93 to -0.48]). Further visual examination of the distance maps indicated that more severe clefts corresponded to increased retrusion in the midface and the tip of the nose, though these differences were not statistically significant across groups. For fusion vs differentiation defects, no statistically significant differences were observed, neither for the complete face nor for any of the individual regions. The findings demonstrate statistically significant differences in the upper lip region between children with UCL and those with UCLP, particularly with greater upper lip retrusion in the UCLP group. The use of color-coded distance maps revealed local variations and a trend of asymmetry in the nasal region, with increasing retrusion of the nose tip, upper lip, and cheeks correlating with the severity of the cleft. Although these trends were not statistically significant, they suggest a progressive facial retrusion pattern as cleft severity increases. For the secondary objective, no statistical differences were found between the facial morphology of children with fusion and differentiation defects, although a similar progression of maxillary retrusion was observed in the distance maps.

Privacy preserving security using multi‐key homomorphic encryption for face recognition

AbstractRecently, face recognition based on homomorphic encryption for privacy preservation has garnered significant attention. However, there are two major challenges with homomorphic encryption methods: the security and efficiency of face recognition systems. We present a more efficient and secure PUM (Privacy preserving security Using Multi‐key homomorphic encryption) mechanism for facial recognition. By integrating feature grouping with parallel computing, we enhance the efficiency of homomorphic operations. The use of multi‐key encryption ensures the security of the facial recognition system. This approach improves the security and speed of facial recognition systems in cloud computing scenarios, increasing the original 128‐bit security to a maximum of 1664‐bit security. In terms of efficiency, comparing encrypted images takes only 0.302 s, with an accuracy rate of 99.425%. When applied to a campus scenario, the average search time for a facial template library containing 700 encrypted features is approximately 1.5 s. Consequently, our solution not only ensures user privacy but also demonstrates superior operational efficiency and practical value. In comparison to recently emerged ciphertext facial recognition systems, our solution has demonstrated notable enhancements in both security and time efficiency.

The Facial Recognition Technology in Academic Attendance: A Comparative Study for Real-Time Management

In today’s academic settings, managing daily tasks like attendance tracking has become increasingly burdensome. Traditional methods of manual attendance taking are inefficient and time-consuming, particularly with growing numbers of students and staff. To address this challenge, various approaches, including face identification systems, have been developed. This research introduces a smart face recognition (FR) system for managing attendance efficiently. The system employs multiple face recognition methodologies such as Local Binary Histogram, PCA/Eigen Face Recognizer, and Fisher Face Recognizer, enhancing system performance. These methods are combined using Ensemble Fusion to improve accuracy. Additionally, the system utilizes Multitask Cascaded Convolutional Network for face detection and attribute extraction. Extracted attributes are matched with stored facial templates to identify recognized faces and mark attendance. Integration with Cloud API facilitates record-keeping. The system also includes a feedback and notification system for process status indication. Results indicate that the proposed system achieves 82.1% accuracy in face recognition and requires minimal time (0.000081s) to predict and mark attendance.

Religious labels and food preferences, but not country of origin, support opposing face aftereffects

Face templates can be experimentally manipulated, and category-contingent aftereffects suggest discrete templates across social groups. We tested whether 1) explicit religious labels, 2) food preferences, and 3) country of origin would support religion-contingent aftereffects across Christians and Muslims face sets. While viewing face images, ninety-three participants heard audio that stated either 1) a character's religious identity, 2) preferred food, or 3) country of origin. Participants viewed contracted Christian faces and expanded Muslim faces during the training phase. To measure adaptation, before and after the training phases, participants selected the face out of a pair of expanded and contracted Christian or Muslim faces that they found more attractive. Contingent aftereffects were found in the religious explicit (t(30) = 2.49, p = 0.02, Cohen's d = 0.58) and food conditions (t(30) = −3.77, p < 0.01, Cohen's d = −0.82), but not the country condition (t(30) = 1.64, p = 0.11, Cohen's d = 0.31). This suggests that religious labels and food preferences create socially meaningful groups, but country of origin does not. This is evidence of an impact of social categorization on visual processing.

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network.

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential.

Fetal face shape analysis from prenatal 3D ultrasound images

3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.

Protected Face Templates Generation Based on Multiple Partial Walsh Transformations and Simhash

Protected Face Templates Generation Based on Multiple Partial Walsh Transformations and Simhash

Comprehensive Vulnerability Evaluation of Face Recognition Systems to Template Inversion Attacks Via 3DFace Reconstruction.

In this paper, we comprehensively evaluate the vulnerability of state-of-the-art face recognition systems to template inversion attacks using 3D face reconstruction. We propose a new method (called GaFaR) to reconstruct 3D faces from facial templates using a pretrained geometry-aware face generation network, and train a mapping from facial templates to the intermediate latent space of the face generator network. We train our mapping with a semi-supervised approach using real and synthetic face images. For real face images, we use a generative adversarial network (GAN)-based framework to learn the distribution of generator intermediate latent space. For synthetic face images, we directly learn the mapping from facial templates to the generator intermediate latent code. Furthermore, to improve the success attack rate, we use two optimization methods on the camera parameters of the GNeRF model. We propose our method in the whitebox and blackbox attacks against face recognition systems and compare the transferability of our attack with state-of-the-art methods across other face recognition systems on the MOBIO and LFW datasets. We also perform practical presentation attacks on face recognition systems using the digital screen replay and printed photographs, and evaluate the vulnerability of face recognition systems to different template inversion attacks. The project page is available at https://www.idiap.ch/paper/gafar.

Building 3D Generative Models from Minimal Data.

We propose a method for constructing generative models of 3D objects from a single 3D mesh and improving them through unsupervised low-shot learning from 2D images. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. As we demonstrate in the face domain, these models can be used to infer 3D reconstructions from 2D data (inverse graphics) or 3D data (registration). Specifically, we show that our approach can be used to perform face recognition using only a single 3D template (one scan total, not one per person). We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images. Our approach is motivated as a potential model for the origins of face perception in human infants, who appear to start with an innate face template and subsequently develop a flexible system for perceiving the 3D structure of any novel face from experience with only 2D images of a relatively small number of familiar faces.

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications.

Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM's application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4-10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.

A face template: Improving the face generation quality of multi-stage generative adversarial networks using coarse-grained facial priors

A face template: Improving the face generation quality of multi-stage generative adversarial networks using coarse-grained facial priors

Holistic processing is modulated by the probability that parts contain task-congruent information.

Holistic processing of face and non-face stimuli has been framed as a perceptual strategy, with classic hallmarks of holistic processing, such as the composite effect, reflecting a failure of selective attention, which is a consequence of this strategy. Further, evidence that holistic processing is impacted by training different patterns of attentional prioritization suggest that it may be a result of learned attention to the whole, which renders it difficult to attend to only part of a stimulus. If so, holistic processing should be modulated by the same factors that shape attentional selection, such as the probability that distracting or task-relevant information will be present. In contrast, other accounts suggest that it is the match to an internal face template that triggers specialized holistic processing mechanisms. Here we probed these accounts by manipulating the probability, across different testing sessions, that the task-irrelevant face part in the composite face task will contain task-congruent or -incongruent information. Attentional accounts of holistic processing predict that when the probability that the task-irrelevant part contains congruent information is low (25%), holistic processing should be attenuated compared to when this probability is high (75%). In contrast, template-based accounts of holistic face processing predict that it will be unaffected by manipulation given the integrity of the faces remains intact. Experiment 1 found evidence consistent with attentional accounts of holistic face processing and Experiment 2 extends these findings to holistic processing of non-face stimuli. These findings are broadly consistent with learned attention accounts of holistic processing.

Preliminary study on three-dimensional morphological reconstruction method for external nose defect based on three-dimensional face template

Objective: To provide a new solution for the digital design of nasal prostheses, this study explores the three-dimensional (3D) facial morphology completion method for external nasal defects based on the non-rigid registration process of 3D face template. Methods: A total of 20 male patients with tooth defect and dentition defect who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to December 2022 were selected, age 18-45 years old. The original 3D facial data of patients were collected, and the 3D facial data of the external nose defect was constructed in Geomagic Wrap 2021 software. Using the structured 3D face template data constructed in the previous research of the research group, the 3D face template was deformed and registered to the 3D facial data of external nose defect (based on the morphology of non-defective area) by non-rigid registration algorithm (MeshMonk program), and the personalized deformed data of the 3D face template was obtained, as the complemented facial 3D data. Based on the defect boundary of the 3D facial data of the external nose defect, the complemented external nose 3D data can be cut out from the complemented facial 3D data. Then the nasofacial angle and nasolabial angle of the complemented facial 3D data and the original 3D facial data was compared and analyzed, the ratio between the nose length and mid-face height, nose width and medial canthal distance of the complemented facial 3D data was measured, the edge fit between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was evaluated, and the morphological difference of the nose between the complemented external nose 3D data and the original 3D facial data was analyzed. Results: There was no significant statistically difference (t=-0.23, P=0.823; Z=-1.72, P=0.086) in the nasofacial angle (28.2°±2.9°, 28.4°±3.5° respectively) and nasolabial angle [95.4°(19.2°), 99.9°(9.5°) respectively] between the 20 original 3D facial data and the complemented facial 3D data. The value of the ratio of nose length to mid-face height in the complemented facial 3D data was 0.63±0.03, and the value of the ratio of nose width to medial canthal distance was 1.07±0.08. The curve deviation (root mean square value) between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was (0.37±0.09) mm, the maximum deviation was (1.14±0.32) mm, and the proportion of the curve deviation value within±1 mm was (97±3)%. The distance of corresponding nose landmarks between the complemented facial 3D data and the original 3D facial data were respectively, Nasion: [1.52(1.92)] mm; Pronasale: (3.27±1.21) mm; Subnasale: (1.99±1.09) mm; Right Alare: (2.64±1.34) mm; Left Alare: (2.42± 1.38) mm. Conclusions: The method of 3D facial morphology completion of external nose defect proposed in this study has good feasibility. The constructed complemented external nose 3D data has good facial coordination and edge fit, and the morphology is close to the nose morphology of the original 3D facial data.

3D craniofacial reconstruction framework using elastic surface deformation based on automatic landmark positioning

AbstractComputer‐aided craniofacial reconstruction (CFR) is a process that aims to estimate facial impressions based on skull remains. It mimics the conventional method with a conceptual model‐based framework. The existing problems in CFR are that landmark annotation is expert‐dependent, landmark processing in the 3D domain has volumetric challenges, and a method based on a population's morphological characteristics (templates). A framework with three stages is proposed: Building a craniofacial model, automatic landmark detection, and surface deformation. Machine learning is deployed to draw local surface correlations as landmarks and automatically detects their position. The local surface context is extracted using the Surface Curvature Feature (SCF) as a 3D descriptor. Using a cluster‐based filter, the average distance (to the ground truth) of the top 20 points is 0.0326 units. Cluster‐based filters are better than mass‐radius‐based filters and consistently give better pinpoint accuracy, especially in multi‐cluster cases. Training data consists of 140,000 SCF for ten landmark classes. The third stage, surface deformation, fits the facial template to the cranial based on the corresponding facial‐cranial landmarks. Five experts from the Anthropology department stated that of the reconstruction results, 91.5% could retain the template details and are accepted as the natural shape of the human face.

Pareidolic faces receive prioritized attention in the dot-probe task

Face pareidolia occurs when random or ambiguous inanimate objects are perceived as faces. While real faces automatically receive prioritized attention compared with nonface objects, it is unclear whether pareidolic faces similarly receive special attention. We hypothesized that, given the evolutionary importance of broadly detecting animacy, pareidolic faces may have enough faceness to activate a broad face template, triggering prioritized attention. To test this hypothesis, and to explore where along the faceness continuum pareidolic faces fall, we conducted a series of dot-probe experiments in which we paired pareidolic faces with other images directly competing for attention: objects, animal faces, and human faces. We found that pareidolic faces elicited more prioritized attention than objects, a process that was disrupted by inversion, suggesting this prioritized attention was unlikely to be driven by low-level features. However, unexpectedly, pareidolic faces received more privileged attention compared with animal faces and showed similar prioritized attention to human faces. This attentional efficiency may be due to pareidolic faces being perceived as not only face-like, but also as human-like, and having larger facial features-eyes and mouths-compared with real faces. Together, our findings suggest that pareidolic faces appear automatically attentionally privileged, similar to human faces. Our findings are consistent with the proposal of a highly sensitive broad face detection system that is activated by pareidolic faces, triggering false alarms (i.e., illusory faces), which, evolutionarily, are less detrimental relative to missing potentially relevant signals (e.g., conspecific or heterospecific threats). In sum, pareidolic faces appear "special" in attracting attention.

Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template

(1) Background: Three-dimensional (3D) facial anatomical landmarks are the premise and foundation of facial morphology analysis. At present, there is no ideal automatic determination method for 3D facial anatomical landmarks. This research aims to realize the automatic determination of 3D facial anatomical landmarks based on the non-rigid registration algorithm developed by our research team and to evaluate its landmark localization accuracy. (2) Methods: A 3D facial scanner, Face Scan, was used to collect 3D facial data of 20 adult males without significant facial deformities. Using the radial basis function optimized non-rigid registration algorithm, TH-OCR, developed by our research team (experimental group: TH group) and the non-rigid registration algorithm, MeshMonk (control group: MM group), a 3D face template constructed in our previous research was deformed and registered to each participant's data. The automatic determination of 3D facial anatomical landmarks was realized according to the index of 32 facial anatomical landmarks determined on the 3D face template. Considering these 32 facial anatomical landmarks manually selected by experts on the 3D facial data as the gold standard, the distance between the automatically determined and the corresponding manually selected facial anatomical landmarks was calculated as the "landmark localization error" to evaluate the effect and feasibility of the automatic determination method (template method). (3) Results: The mean landmark localization error of all facial anatomical landmarks in the TH and MM groups was 2.34 ± 1.76 mm and 2.16 ± 1.97 mm, respectively. The automatic determination of the anatomical landmarks in the middle face was better than that in the upper and lower face in both groups. Further, the automatic determination of anatomical landmarks in the center of the face was better than in the marginal part. (4) Conclusions: In this study, the automatic determination of 3D facial anatomical landmarks was realized based on non-rigid registration algorithms. There is no significant difference in the automatic landmark localization accuracy between the TH-OCR algorithm and the MeshMonk algorithm, and both can meet the needs of oral clinical applications to a certain extent.

Occlusion of faces by sanitary masks improves facial attractiveness of other races.

Recent studies provide mixed results regarding whether the perception of facial attractiveness is increased or decreased by partial occlusion with a sanitary mask. One set of studies demonstrated that occluding the bottom half of a face increased facial attractiveness. This effect is thought to occur because the occluded area is interpolated by an average facial representation that is perceived as attractive. However, several groups of studies showed that partial occlusion can increase or decrease perceived attractiveness depending on the attractiveness of the original (unoccluded) face, due to regression to the mean. To reconcile this inconsistency, we propose that the occluded area is interpolated not by an average facial representation, but by a template of moderate attractiveness, shaped by the distribution of each viewer's experience. This hypothesis predicts an interaction between occlusion and the attractiveness of the original face so that occluded attractive faces are rated as less attractive, while occluded unattractive faces are rated as more attractive. To examine this hypothesis, the present study used attractiveness-rating tasks with mask-free versus masked faces in own-race and other-races categories. Viewers were familiar with own-race faces and unfamiliar with other-races faces. If moderate-attractiveness interpolation were the explanatory factor, the interaction between the occlusion and the attractiveness of the original face should be found only in the rating of own-race faces. Consistent with this hypothesis, the interaction between the occlusion and the attractiveness of the original faces was significant only for the own-race faces. Specifically, wearing a sanitary mask decreased the facial attractiveness of attractive faces in the own-race, while it increased the attractiveness regardless of the level of facial attractiveness in other-races. These findings suggest that the occluded area of own-race faces is interpolated by a facial template of moderate attractiveness. The other-races template could be developed using familiar exemplars such as celebrities. Thus, interpolation by such a template should result in elevated attractiveness relative to that by an own-race template. Accordingly, the apparent inconsistency in the literature regarding the effect of partial occlusion on physical attractiveness can be explained in terms of differences in the template involving interpolation of the occluded area.

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.

Face Templates Encryption Technique Based on Random Projection and Deep Learning

Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait (in different feature domains) as the only authentication factor, in addition to achieving good performance with high security. The standard Generative Adversarial Network (GAN) is proposed as an encryption tool that needs the cancelable key during the training phase, and the testing phase depends only on the biometric trait. Additionally, random projection transformation is employed to increase the proposed system’s security and performance. The proposed transformation system is tested using the standard ORL face database, and the experiments are done by applying different features domains. Moreover, a security analysis for the proposed transformation system is presented.

Individual differences in classification images of Mooney faces.

Human face recognition is robust even under conditions of extreme lighting and in situations where there is high noise and uncertainty. Mooney faces are a canonical example of this: Mooney faces are two-tone shadow-defined images that are readily and holistically recognized despite lacking easily segmented face features. Face perception in such impoverished situations-and Mooney face perception in particular-is often thought to be supported by comparing encountered faces to stored templates. Here, we used a classification image approach to measure the templates that observers use to recognize Mooney faces. Visualizing these templates reveals the regions and structures of the image that best predict individual observer recognition, and they reflect the underlying internal representation of faces. Using this approach, we tested whether there are classification images that are consistent from session to session, whether the classification images are observer-specific, and whether they allow for pattern completion of holistic representations even in the absence of an underlying signal. We found that classification images of Mooney faces were indeed non-random (i.e., consistent session from session) within each observer, but they were different between observers. This result is in line with previously proposed existence of face templates that support face recognition, and further suggests that these templates may be unique to each observer and could drive idiosyncratic individual differences in holistic face recognition. Moreover, we found classification images that reflected information within the blank regions of the original Mooney faces, suggesting that observers may fill in missing information using idiosyncratic internal information about faces.