Age Invariant Face Recognition Research Articles

Age-Invariant Face Recognition Using FaceNet and Multi-task Cascaded Convolutional Networks (MTCNN)

In an era where face recognition technology serves as a cornerstone for security, surveillance, and human-computer interaction, the persistence of age-related variations in facial appearance presents a significant challenge. This research introduces an innovative solution that amalgamates the capabilities of the FaceNet deep learning model with Multi-task Cascaded Convolutional Networks (MTCNN) to achieve precise and robust face recognition across diverse age groups. Leveraging FaceNet's prowess in extracting distinctive features from facial images and mapping them to a high-dimensional feature space for efficient face matching, our system incorporates MTCNN as a pre-processing step to accurately detect and align faces, effectively mitigating age-related changes in facial geometry. Crucially, this novel approach obviates the need for age-specific databases and age group categorization, rendering it a versatile and practical solution for age-invariant face recognition. Rigorous experimentation on benchmark datasets underscores the system's resilience and accuracy across a broad spectrum of age groups, culminating in state-of-the-art results in age-invariant face recognition. The implications of this proposed approach are profound, promising to fortify security measures and enhance user experiences in applications such as access control, personal identification, and customer service, while ensuring dependable and accurate face recognition, irrespective of an individual's age. Key Words: Face recognition, face aging, generative adversarial networks

Age-invariant face network (AFN): a discriminative model towards age-invariant face recognition

Age-invariant face network (AFN): a discriminative model towards age-invariant face recognition

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face Recognition

Face recognition techniques have been widely employed in real-world biomimetics applications. However, traditional approaches have limitations in recognizing faces correctly with large age differences because of significant changes over age in the same person, leading to unsatisfactory recognition performance. To address this, previous studies propose to decompose and identify age and identity features independently in facial images across diverse age groups when optimizing the discriminative model so as to improve the age-invariant face recognition accuracy. Nevertheless, the interrelationships between these features make it difficult for the decomposition to disentangle them properly, thus compromising the recognition accuracy due to the interactive impacts on both features. To this end, this paper proposes a novel deep framework that incorporates a novel Hybrid Spatial-Channel Attention Module to facilitate the cross-age face recognition task. Particularly, the proposed module enables better decomposition of the facial features in both spatial and channel dimensions with attention mechanisms simultaneously while mitigating the impact of age variation on the recognition performance. Beyond this, diverse pooling strategies are also combined when applying those spatial and channel attention mechanisms, which allows the module to generate discriminative face representations while preserving complete information within the original features, further yielding sounder recognition accuracy. The proposed model is extensively validated through experiments on public face datasets such as CACD-VS, AgeDB-30, and FGNET, where the results show significant performance improvements compared to competitive baselines.

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Age-invariant face recognition based on identity-age shared features

Age-invariant face recognition based on identity-age shared features

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and a New Benchmark.

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR lacks visual results for model interpretation and FAS compromises downstream recognition due to artifacts. Therefore, we propose a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn the age-invariant identity-related representation for face recognition while achieving pleasing face synthesis for model interpretation. Specifically, we propose an attention-based feature decomposition to decompose the mixed face features into two uncorrelated components-identity- and age-related features-in a spatially constrained way. Unlike the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, which can improve the age smoothness of synthesized faces through a weight-sharing strategy. Benefiting from the proposed multi-task framework, we then leverage those high-quality synthesized faces from FAS to further boost AIFR via a novel selective fine-tuning strategy. Furthermore, to advance both AIFR and FAS, we collect and release a large cross-age face dataset with age and gender annotations, and a new benchmark specifically designed for tracing long-missing children. Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance than state-of-the-art methods for both AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, obtaining competitive performance on face recognition in the wild. The source code and datasets are available at http://hzzone.github.io/MTLFace.

Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions

Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions

LIAAD: Lightweight attentive angular distillation for large-scale age-invariant face recognition

Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architectures for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee robustness. This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, LIAAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, LIAAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our LIAAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach on light-weight structure. This work also presents a new longitudinal face aging (LogiFace) database 1This database will be made available1 for further studies in age-related facial problems in future.

Age invariant face recognition using discriminative tensor subspace learning with fuzzy based classification

AbstractAging is a complex process that affects both the shape and texture of a face. In the aging domain, various methods have been proposed for age estimation or simulation of the face based on an image at a given age. Face recognition methods that are designed for automatic face recognition are still a challenging issue. We present a novel age invariant face recognition method using tensor subspace learning with fuzzy synthetic classification. Local Binary Pattern (LBP) processed face images are used to learn the tensor sub‐space, which converts high dimensional feature space to age‐invariants while retaining key elements of the local geometrical structure of the face. Tensor sub‐space is better in terms of sub‐space learning and feature extraction than Principal Component Analysis (PCA)/Linear Discriminant Analysis (LDA) that been used in many techniques in the literature. Tensor Normalized Face images are generated which exhibit maximum inter‐class distances and minimum intra‐class distances. Further local patches are used for synthesizing global Fuzzy membership scores to classify the test face images. Experiments performed on standard face‐aging datasets, namely FG‐NET, AGEDB, and MORPH‐Album‐II, and received accuracy of 99.15%, 99.20%, and 99.8%, respectively. Experimental results outperform the current state‐of‐the‐art techniques, and results show the promise of the proposed system for personal identification despite the aging process. It also proved that the local descriptor gives better performance over the global descriptor like PCA for the aging process. The method also demonstrated improved performance as compared with compute‐intensive methods that required training on deep networks.

Age Invariant Face Recognition Model Based on Convolution Neural Network (CNN)

Building an intelligent system similar to the human perception system in face recognition is still an active area of research, despite the advancements in technologies and face recognition research carried out when age changes. Deep learning algorithms have outperformed conventional methods in with regard to accuracy and effectiveness of recognition a variety of difficulties, including position, expression, lighting, and aging. But aging is one of the problems that affects the face the most, as it plays a significant role which directly affects facial features, so we notice some people who are very difficult to distinguish and may not be known at all because of the strong change in their features. As a result, we researched deep learning techniques generally and the convolutional neural network (CNN) specifically. This strategy is employed by a number of significant stages: The first side, includes preparing the dataset related to the subject of the study, Isolate the data between training, validation and testing. As for the second part of the work, data preprocessing, such a data augmentation, Normalization, Face detection, and resizing. After then, begin a features extraction operation by the convolution neural network (CNN) that is suggested. After all that, the classification stage begins, which was done by using the (SoftMax) function, because we have approximately (570) classes. In the testing phase, we perform the task of checking the two images entered whether they belong to the same person or not. In this paper, adopted the (Age) and (FG-Net) datasets, Finally, the verification accuracy rate for the proposed system reached 98.7 % on the (Age) dataset, and reached 99.4 % on the (FG-Net) dataset.

Missing Person Identification

At present the Computer automated Face recognition systems are used for personal identification, but the Age variations of an individual poses a serious problem for it. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. To classify person age using faces author using combination of two CNN where one CNN will extract face features which can help in identify changes in face over time and second CNN helps in predicting/ classifying age. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components—identity- and age-related features—through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. d. This system will decrease the crimes and ensure the security in our society.

Face recognition under large age gap using age face generation

Age invariant face recognition (AIFR) is a challenging problem in the area of the face recognition. To handle large age gap for face recognition, we proposed a robust approach based on deep learning for face recognition under a large age gap. The presented approach consists of four important steps. The pre-processing is done for face detection. Age face generation is processed with the help of modified age conditional generative adversarial network (acGAN). Generated age face images are mixed with train dataset and augmentation is applied to increase the size of training data for handling biasness of the deep learning models towards dataset size. A modified residual convolutional neural network is applied for training and testing of face images. The performance has been evaluated using two-fold cross-validation on standard and challenging LAG dataset. The proposed approach achieved the 92.5% recognition accuracy, which is better than the existing face recognition approaches for a large age gap.

ChildPredictor: A Child Face Prediction Framework With Disentangled Learning

The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by "style", i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found at https://github.com/zhaoyuzhi/ChildPredictor.

Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model

Facial aging variation is a challenging process in the design of face recognition system because of high intra-personal differences instigated by age progression. Age-invariant face recognition (AIFR) models find applicability in several real time applications such as criminal identification, missing person detection, and so on. The main issue is the high intra-personal disparities because of complicated and non-linear age progression process. An essential component of face recognition model is the extraction of important features from the facial images for reducing intrapersonal differences produced by illumination, expression, pose, age, etc. The recent advances of machine learning (ML) and deep learning (DL) models pave a way for effective design of AIFR models. In this view, this study presents a new Bald Eagle Search Optimization with Deep Transfer Learning Enabled AFIR (BESDTL-AIFR) model. The presented BESDTL-AIFR model primarily pre-processes the facial images to enhance the quality. Besides, the BESDTL-AIFR model utilizes Inception v3 model for learning high level deep features. Next, these features are passed into the optimal deep belief network (DBN) model for face recognition. Finally, the hyperparameters of the DBN model are optimally chosen by the use of BES algorithm. Experimentation analysis on challenging benchmark datasets pointed out the promising outcomes of the BESDTL-AIFR model compared to recent approaches. • Develop a deep learning based Age-Invariant Face Recognition model. • Employ Inception v3 feature extractor with DBN classification model. • Present bald eagle search optimization based hyperparameter tuning process. • Validate the performance of BESDTL-AIFR model on UTKFace and CACD dataset.

An approach to enhance performance of age invariant face recognition

Age invariant face recognition (AIFR) is currently a study topic with several potential uses. It offers a variety of real-world applications, including passport renewal, driver’s license renewal, locating missing children, locating criminals, providing security to VIPs etc. In the field of AIFR, scientific efforts have increased. Matching faces of big age differences is thus a challenge, owing to the significant variation in appearance between young and elderly age. The appearance and form of the face deteriorate with age, making facial recognition the most difficult task. AIFR has become a highly common and difficult chore in recent years. In this discipline, the set of feature extraction and classification algorithms is crucial. This paper addresses the issues raised above by proposing an enhanced ASM approach for extracting features from 2D search regions using handcrafted and deep image features in conjunction with a 7-layer CNN architecture and a smaller image size of 32x32 pixels to reduce delay time and space complexity. Using the standard dataset LAG, the study approach entails running many tests to evaluate the proposed system’s performance. The results show that the suggested method beats state-of-the-art algorithms in face recognition and achieves good accuracy throughout the age spectrum. The presented methodology achieves a maximum accuracy of 91.76 percent for the LAG database, outperforming all existing state-of-the-art methodologies.

Discriminative aging subspace learning for age estimation

Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject’s age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.

A robust approach based on local feature extraction for age invariant face recognition

A robust approach based on local feature extraction for age invariant face recognition

Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention

Different from general face recognition, age-invariant face recognition (AIFR) aims at matching faces with a big age gap. Previous discriminative methods usually focus on decomposing facial feature into age-related and age-invariant components, which suffer from the loss of facial identity information. In this article, we propose a novel Multi-feature Fusion and Decomposition (MFD) framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants. Specifically, we first sample multiple face images of different ages with the same identity as a face time sequence. Then, the multi-head attention is employed to capture contextual information from facial feature series, extracted by the backbone network. Next, we combine feature decomposition with fusion based on the face time sequence to ensure that the final age-independent features effectively represent the identity information of the face and have stronger robustness against the aging process. Besides, we also mitigate imbalanced age distribution in the training data by a re-weighted age loss. We experimented with the proposed MFD over the popular CACD and CACD-VS datasets, where we show that our approach improves the AIFR performance than previous state-of-the-art methods. We simultaneously show the performance of MFD on LFW dataset.

Implicit and Explicit Feature Purification for Age-Invariant Facial Representation Learning

This paper presents a new method, named implicit and explicit feature purification (IEFP), for age-invariant face recognition. Facial features extracted from a face image contain the information about the identity, age, and other attributes. For age-invariant face recognition, it is important to remove the irrelevant information, and retain the identity information only, in the facial features. Through the two proposed feature purification mechanisms, our framework can produce facial-feature embeddings that preserve identity information as much as possible and are insensitive to age variations. Specifically, on the one hand, a special network module is devised to implicitly purify the original facial features obtained from a face encoder. On the other hand, to obtain purer facial feature representations for age-invariant face recognition, irrelevant information within the implicitly purified features, such as the age, is further removed. This is realized by using a regularizer, based on information theory, to explicitly minimize the correlation between identity-related features and age-related features. Comprehensive ablation studies show that these two feature purification schemes can work independently, as well as collaboratively, to achieve better performance. Extensive evaluations on several benchmark data sets show that the IEFP method is on par with those competitors learned on far more favorable training samples, and it achieves the best performance in a fair comparison. Furthermore, we provide mathematical interpretation to explain the effectiveness of our approach, and find that it tends to generate low-rank, yet high-dimensional, representations for age-invariant face recognition.