Face Image Samples Research Articles

Deep Composite Face Image Attacks: Generation, Vulnerability and Detection

Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate 526 unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: https://github.com/jagmohaniiit/LatentCompositionCode.

Investigating subtle changes in facial expression to assess acute pain in Japanese macaques

Changes in facial expression provide cues for assessing emotional states in mammals and may provide non-verbal signals of pain. This study uses geometric morphometrics (GMM) to explore the facial shape variation in female Japanese macaques who underwent experimental laparotomy. Face image samples were collected from video footage of fourteen macaques before surgery and 1, 3, and 7 days after the procedure. Image samples in the pre-surgical condition were considered pain-free, and facial expressions emerging after surgery were investigated as potential indicators of pain. Landmarks for shape analysis were selected based on the underlying facial musculature and their corresponding facial action units and then annotated in 324 pre-surgical and 750 post-surgical images. The expression of pain is likely to vary between individuals. Tightly closed eyelids or squeezed eyes and lip tension were the most commonly observed facial changes on day 1 after surgery (p < 0.01974). A good overall inter-rater reliability [ICC = 0.99 (95% CI 0.75–1.0)] was observed with the method. The study emphasizes the importance of individualized assessment and provides a better understanding of facial cues to pain for captive macaque care.

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Traditional face feature recognition methods have a few problems, such as low accuracy of image feature recognition and high time consumption of image feature recognition. This paper introduces augmented reality technology to realize natural feature recognition of multi pose face images. The multi pose features of face image are obtained by augmented reality technology, the color normalization of face image is realized by gamma algorithm, and the natural features of multi pose face image are extracted by three-dimensional registration technology. The feature error reconstruction of multi pose face image is realized according to sparse representation, the face image samples are projected into the optimal discriminant vector space by using two-dimensional linear discriminant analysis algorithm, and the natural feature recognition of multi pose face image is completed according to augmented reality technology. The experimental results show that the comprehensive accuracy of facial image expression recognition is as high as 95%, and the average error rate of natural feature recognition is only 3%; The time taken for feature recognition is only 3.5s, which shows that the method used in this study has higher efficiency of natural feature recognition of face image.

FuseKin: Weighted image fusion based kinship verification under unconstrained age group

Kinship verification using facial images is mainly performed with a single face sample per person. To perform with a single sample, it is very difficult to specify an age group where kin pairs may have higher similarities. To address the above problem, we propose a novel weighted multi sample fusion (WMSF) method. The proposed WMSF method combines kin signals present in multiple samples per person (MSPP) to form a FuseKin image. To select the most discriminant features from the extracted feature vector, we propose a patch based discriminative analysis (PDA) method. Weights are calculated using the PDA method so as to reduce the discrimination between positive FuseKin pairs. Experiments were conducted on two different datasets which contain multiple face image samples per person, namely Family101 and Family in the Wild (FIW) to validate the performance of the proposed methods. Our method achieves competitive results as compared to other state-of-the-art methods.

2D facial landmark localization method for multi-view face synthesis image using a two-pathway generative adversarial network approach.

One of the key challenges in facial recognition is multi-view face synthesis from a single face image. The existing generative adversarial network (GAN) deep learning methods have been proven to be effective in performing facial recognition with a set of pre-processing, post-processing and feature representation techniques to bring a frontal view into the same position in-order to achieve high accuracy face identification. However, these methods still perform relatively weak in generating high quality frontal-face image samples under extreme face pose scenarios. The novel framework architecture of the two-pathway generative adversarial network (TP-GAN), has made commendable progress in the face synthesis model, making it possible to perceive global structure and local details in an unsupervised manner. More importantly, the TP-GAN solves the problems of photorealistic frontal view synthesis by relying on texture details of the landmark detection and synthesis functions, which limits its ability to achieve the desired performance in generating high-quality frontal face image samples under extreme pose. We propose, in this paper, a landmark feature-based method (LFM) for robust pose-invariant facial recognition, which aims to improve image resolution quality of the generated frontal faces under a variety of facial poses. We therefore augment the existing TP-GAN generative global pathway with a well-constructed 2D face landmark localization to cooperate with the local pathway structure in a landmark sharing manner to incorporate empirical face pose into the learning process, and improve the encoder-decoder global pathway structure for better representation of facial image features by establishing robust feature extractors that select meaningful features that ease the operational workflow toward achieving a balanced learning strategy, thus significantly improving the photorealistic face image resolution. We verify the effectiveness of our proposed method on both Multi-PIE and FEI datasets. The quantitative and qualitative experimental results show that our proposed method not only generates high quality perceptual images under extreme poses but also significantly improves upon the TP-GAN results.

Open Set Sheep Face Recognition Based on Euclidean Space Metric

As the essential content of intelligent animal husbandry, identifying each livestock is the only way to achieve modern and refined scientific husbandry. This paper proposes a sheep face recognition method based on European spatial metrics and realizes noncontact sheep identity recognition by training the network using sheep face image samples in the natural environment. The SheepBase data set was first proposed in this process, which contains 6559 images of Inner Mongolia fine-wool sheep and Sunite sheep. To enhance the diversity of the data, the sheep face images were data-enhanced. Secondly, to solve the problems of more redundant information in the sheep face image and the poor posture and angle of the sheep face, we propose the sheep face detection and correction (SheepFaceRepair) method. This method aims to detect the sheep face area in the image to be recognized and align the sheep face area. On this basis, we offer an open sheep facial recognition network (SheepFaceNet) based on the European spatial metric. This method incorporates the biological identity information features of the sheep face to achieve sheep identity. We also tested the effectiveness of this method in the SheepBase data set. The experimental results show that the method proposed in this paper is much higher than the other methods, and the precision of recognition reaches 89.12%. In addition, we found that integrating the biometrics of the sheep face can effectively improve the network’s recognition capacity.

Application of Multiscale Facial Feature Manifold Learning Based on VGG‐16

Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet‐5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet‐5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.

Semi‐supervised uncorrelated dictionary learning for colour face recognition

Colour images are increasingly used in the fields of computer vision, pattern recognition and machine learning, since they can provide more identifiable information than greyscale images. Thus, colour face recognition has attracted accumulating attention. Its key problem is how to remove the similarity between colour component images and take full advantage of colour difference information. Decision‐level similarity reduction between colour component images directly affects the recognition effect, but it has been found in no work. In this study, the authors propose a novel colour face recognition approach named semi‐supervised uncorrelated dictionary learning (SUDL), which realises decision‐level similarity reduction and fusion of all colour components in face images. SUDL employs the labelled and unlabelled colour face image samples into structured dictionary learning to achieve three uncorrelated discriminating dictionaries corresponding to three colour components of face images, and then uses these dictionaries and the sparse coding technique to make a classification decision. Experimental results in multiple public colour face image databases demonstrate that the dictionary decorrelation, structured dictionary learning and unlabelled samples used in the proposed approach are effective and reasonable, and the proposed approach outperforms several representative colour face recognition methods in recognition rates, despite of its poor time performance.

The application and design of neural computation in visual perception

Visual perception is an important way for organisms to perceive the external world. Simulation of visual cognitive process can enhance the cognitive ability of machine vision. Therefore, how to simulate visual perception system and make computer have a high world understanding ability is a hotspot of current neurocomputing. Based on the information processing mechanism of visual perception system, this paper establishes a neural computing model based on visual perception mechanism. The simulation results of standard face database and natural landscape images show that the proposed method can recognize face samples better when other noise samples are added to the face image samples. In landscape contour fitting simulation, the results show that although this method has little advantage for large contour image recognition, but for small contour recognition, this method is obviously superior to other methods.

Three-dimensional image reconstruction method for human maxillofacial defect images based on block sample feature matching

In traditional methods, template matching of human face image sample and edge contour segmentation is ignored, and the application is susceptible to the robustness of skin colour features and pose changes, resulting in poor accurate three-dimensional reconstruction and recognition of human maxillofacial defect images. Therefore, a three-dimensional image reconstruction method for human maxillofacial defect images based on block sample feature matching is proposed. A feature matching template of human maxillofacial images is constructed, and template matching and three-dimensional edge contour segmentation are performed to the sample images through the blocking technique. The simulation results show that this method has good feature detection and detection ability in three-dimensional reconstruction of human maxillofacial defect images and can provide high accuracy in three-dimensional reconstruction.

Face recognition based on texture information and geodesic distance approximations between multivariate normal distributions

Geodesic distance is a natural dissimilarity measure between probability distributions of a specific type, and can be used to discriminate texture in image-based measurements. Furthermore, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to be used as texture dissimilarity metrics in the context of face recognition. A novel face recognition approach based on texture discrimination in high-resolution color face images is proposed, unlike the typical appearance-based approach that relies on low-resolution grayscale face images. In our face recognition approach, sparse facial features are extracted using predefined landmark topologies, that identify discriminative image locations on the face images. Given this landmark topology, the dissimilarity between distinct face images are scored in terms of the dissimilarities between their corresponding face landmarks, and the texture in each one of these landmarks is represented by multivariate normal distributions, expressing the color distribution in the vicinity of each landmark location. The classification of new face image samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score, using the nearest neighbor rule. The proposed face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates. Moreover, the proposed texture dissimilarity metric also is efficient in general texture discrimination (e.g. texture recognition of material images), as our experiments suggest.

Study on color space for the performance of degraded face image recognition

Color provides important information and features for face recognition. Different color spaces possess different characteristics and are suitable for different applications. In this paper, we propose to investigate how different color space components influence the performance of degraded face recognition. Towards this goal, nine color space components are selected for the evaluation. In addition, four different types of image-based degradations are applied to face image samples in order to discover the impact on the performance of face recognition. The experimental results show that, all selected color components have similar influence to the performance of face recognition system depend on the acquisition devices and the experimental setups.

Face recognition using support vector model classifier for user authentication

Most existing user authentication approaches for detecting fraud in e-commerce applications have focused on Secure Sockets Layer (SSL)-based authentication to inspect a username and a password from a server, rather than the inspection of personal biometric information. Because of the lack of support for mutual authentication or two-way authentication between a consumer and a mercantile agent, one-way SSL authentication cannot prevent man-in-the-middle attacks. In practice, in user authentication systems, machine learning and the generalisation capability of support vector models (SVMs) are used to guarantee a small classification error. This study developed an online face-recognition system by training an SVM classifier based on user facial features associated with wavelet transforms and a spatially enhanced local binary pattern. A cross-validation scheme and SVMs associated with the Olivetti Research Laboratory database of user facial features were used for solving classification precision problems. Experimental results showed that the classification error decreased with an increase in the size of the training samples. By using the aggregation of both the low-resolution and the high-resolution face image samples, the global precision of face recognition was over 97% with tenfold cross-validation scheme for an image data size of 168 and 341, respectively. Overall, the proposed scheme provided a higher precision of face recognition compared with the average precision for low-resolution face image (approximately 89%) of the existing schemes.