Using Siamese Networks with Transfer Learning for Face Recognition on Small-Samples Datasets

For the past years, studies on face recognition were focused on the effects and influence of illumination, pose, and angle to the performance, which, in fairness, has been proven by different research works as the dominant factor of adding complexity to face recognition across age progression observed through photographs, specifically headshots (Ling, Soatto, Ramanathan, & Jacobs, 2007; Ramanathan & Chellappa, 2006; Ramanathan & Chellappa, 2005). However, studies on face verification and recognition with time lapses between pairs of photographs and the interaction on the sexual orientation of the identifier was somehow defocused on the factor of effects of age progression or the time interval between pairs of facial images on face recognition. Thus, the researchers aimed to resolve the gap on the prevailing literatures on face recognition by finding the effects of age interval of the stimuli and sexual orientation of the participants on face recognition using Filipino college students as sample. Results showed that the greater the age gaps between the pair of images, the lesser the chance of identifying it. In addition, it is also not important to consider whether identifier is male or female in terms of recognizing picture because based from the result of the experiment, sexual orientation does not have an effect on the performance of the participants in face recognition. The study was guided by the theory of Biederman (1987) and Recognition By Components (RBC) to analyze the findings.

Facial recognition has become an important technology in the domain of biometric authentication, thereby providing greater protection to access control and identity verification applications. This work presents a facial verification system that is new and based on a Siamese neural network. Its operation is based on having deep convolutional architectures that extract strong feature embeddings for facial images. The proposed system employs the Labeled Faces in the Wild (LFW) dataset for training negative samples and was further enhanced using custom-captured positive and anchor images taken from the webcam with extensive data augmentation to mimic real-life variations in illumination, pose, and occlusion. The architecture of the network consists of an embedding layer to extract 4096-dimensional feature vectors, an L1 distance layer that measures the similarity between pairs of images, and a classification layer to predict the verification outcomes using a binary cross-entropy loss function optimized through the Adam algorithm. Experimental results indicate high precision and recall, showing near-unity performance on benchmark tests and real-time verification scenarios. Those results further highlight the capability of Siamese networks for one-shot learning in biometric applications, minimizing their dependence on large labeled datasets. Future work will focus on the scalability to larger and more heterogeneous datasets and integration with multimodal biometric systems. Generally, this research presents a promising framework for secure, efficient, and robust facial verification for real-world applications.

Person-Specific Face Spoofing Detection Based on a Siamese Network

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.

Great progress in face recognition technology has been made recently. Since the first face recognition vendor test (FRVT)Phillips et al. (2007) in 1993, face recognition performance has been improved by two orders of magnitude in thirteen years. Notably, in the FRVT 2006 it is the first time that algorithms are capable of human performance levels, and at false acceptance rates in the range of 0.05, machines can outperform humans Phillips et al. (2007). The advances are promising for face verification applications where a typical one-to-one match is performed. It is still a grand challenge to power large-scale face image retrieval. Large-scale face image retrieval is the enabling technology behind the next generation search engines (search beyondwords), bywhich web users can do social searchwith personal photos. High performance face identification algorithms are needed to support large scale face image retrieval. Compared with face verification, face identification is believed N times harder than face verification due to its nature of 1:N problems. The number of individuals N in the database has a great impact on both the effectiveness and efficiency of face identification algorithms. With the state of the art face identification algorithms, the identification rate is only around 70% (rank = 1) for the FERET database, a gallery of ten thousands individuals. When to serve for large-scale face image retrieval applications, the identification rate will further decrease as the gallery size increase (fortunately not linearly but logarithmically).The computing complexity of face identification is linearly related to the number of individuals N. For largescale face image retrieval the efficiency of face identification is a key issue. In this paper we focus on the efficiency aspects of face identification. Technically, it is very challenging to find a person from a very large or extremely large database which might hold face images of millions or hundred millions people. A highly efficient image retrieval technology is needed. Indexing technology based on tree structures has been widely used in commercial search engines. These structures are quite efficient for small dimensions (of the order of 1-10). However, as the data dimensionality increases, the query performance of these structures degrades rapidly. For instance, White and Jain report that as the dimensionality increases from 5 to 10, the performance of a nearest-neighbor query 2

Nowadays, Machine-based face recognition is becoming very commonplace, robust and dependable system that is widely employed in numerous cases for access control. As in traditional approach, face recognition needs the extraction of face features prior to classification and recognition, which affects recognition rate. We employ Face Verification checks whether the pictures are associated with a single individual, whereas Face Identification must identify a specific face from a collection of known profiles in the system. To tackle this question, this paper incorporates the CNN structure Alexnet to obtain face identification.Throughout this article, we perform facial recognition using transfer learning in a Siamese network composed of 2 comparable CNNs. A pair of 2 face picture is fed into the Siamese network as input, after which the network learns the traits of this pair of pictures.Next the network is trained using the PRelu activation function to find the ideal learning algorithm and maximal values. Then, the face was identified and categorized. Library Multi-Spectral Face Data - set and Library 2D Faceprint Database were used to test the methodology, it enhances the accuracy of face recognition when compared to algorithms trained on datasets with a particular dataset and a specific spectrum’s recognition rate peaked up to 98 percent.

Face recognition (FR) is a method that uses face feature analysis and comparison to identify or verify individuals. Siamese neural networks (SNNs) are an effective method for FR, providing high accuracy and versatility, especially in situations where data is restricted. Unlike standard neural networks, SNNs learn to distinguish between pairs of inputs rather than individual inputs. However, detecting and recognizing faces in unconstrained environments poses a significant challenge due to various factors such as head pose, illumination, and facial expression variations. The aim of this paper is to design and develop an efficient approach based on SNNs and Transfer Learning methods. For this purpose LFW dataset and transfer learning architectures like VGG-16, EfficientNet, RestNet50 and ConvNext have been utilised. Performance of the architectures were measured using 5-Fold cross validation. According to results, EfficientNet, RestNet50 and ConvNext produced 78% accuracy, 95% and 93 % accuracy respectively. SNN with VGG-16 exhibited a low loss and produced the best accuracy in face recognition with 96%.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.

In the biometrics community, face and speaker recognition are mature fields in which several systems have been proposed over the past twenty years. While existing systems perform well under controlled recording conditions, mismatch caused by the use of different sensors or a lack of cooperation from the subject still significantly affects performance, especially in challenging scenarios such as in forensics. Furthermore, existing methods suffer from scalability issues, which prevents them from taking advantage of increasingly large amounts of training data. This is otherwise a promising approach to improve accuracy in such challenging scenarios. In this thesis we address these problems of mismatch and complexity by developing scalable probabilistic models that we apply to face, speaker and bimodal recognition. Our contributions are four-fold. First, we propose a unified framework for session variability modeling techniques based on Gaussian mixture models (GMM), that encompasses inter-session variability (ISV) modeling, joint factor analysis (JFA) and total variability (TV) modeling. Second, we propose a novel exact and scalable formulation of probabilistic linear discriminant analysis (PLDA), which is a probabilistic and generative framework that models between-class and within-class variations. This formulation solves a major scalability issue, by improving both the time complexity of the training procedure from cubic to linear with respect to the number of samples per class, and the complexity of the scoring procedure. Furthermore, the implementations of all the proposed techniques are integrated into a novel collaborative open source software library called Bob 1 that enforces fair evaluations and encourages reproducible research. Fourth and finally, large-scale experiments are conducted with all of the above machine learning algorithms on several databases such as FRGC for face recognition, NIST SRE12 for speaker recognition and MOBIO for bimodal recognition, showing competitive performances.

Secure Learning using Face Recognition has gained significant attention due to its potential solution in curbing and elimination of electronic examination impersonation through enrollment, authentication and authorization. Despite the advancements, existing system often focuses on individual entities, void of models and special dataset to enhance security measure in comprehensively curbing electronic examination impersonation. This study aims to address this gap by performing a model using face recognition: Labeled Face in the Wild (LFW), Custom Dataset, algorithms using TensorFlowJs and Face Application Programming Interface (API) model. The research evaluates this model on a unified facial recognition dataset, assessing their performance based on face verification and recognition. The LFW dataset a popular benchmark dataset for face verification and recognition focuses on detecting and alignment of captured images, where TensorFlowJs extract face embedding, while the Face API verifies captured image by loading the Custom Dataset (Enrolled images) comparing with that of the recent before successful authentication. The LFW-TensorFlowJs combines these approaches to leverage both Secure and accurate information. Results shows that integrating LFW, TensorFlowJs and Face API model outperforms the standalone LFW.

Face recognition has received more attention in the recent past. It refers to using biometric technology to identify individuals from a captured image by comparing it to the images in the database. There are three face recognition techniques: 2D, 2D-3D and 3D. Face recognition occurs in three processes. Firstly, face recognition begins with face detection, where an image is identified as having a face. That is followed by face extraction, which involves identifying the various faces within an image. The final stage is face classification which entails face verification or face identification. Depending on the type of system, face recognition can either occur in verification or identification mode. Additionally, face recognition has various applications in the current global environment. Face recognition can be used in security systems, hospitals, schools, and retail industries. It allows easier verification and identification of individuals. However, despite the development of the technology, there are still some challenges, such as plastic surgery, illumination, aging, occlusion and pose variation.

Biometric systems enable people to distinguish between physical and behavioral characteristics. Face recognition systems, a type of biometric systems, use peoples’ facial features to recognize them. The aim of this study is to perform face recognition and verification system that can run on mobile devices. The developed application is based on comparing the faces in two photographs. The user uploads two photos to the system, the system identifies the faces in these photos and performs authentication between the two faces. As a result, the system gives the output that the two faces in the photo belong to the same or different persons. It provides a security measure thanks to the face identification and verification feature included in this application. This application can be integrated into various applications and used in systems such as user login.

Event Abstract Back to Event Autistic traits and adaptive coding of face identity Gillian Rhodes1*, Linda Jeffery1, Libby Taylor1 and Louise Ewing1 1 ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia, Australia Background Our ability to discriminate and recognize thousands of faces despite their similarity as visual patterns relies on adaptive, norm-based, coding mechanisms that are continuously updated by experience. Reduced adaptive coding of face identity has been proposed as a neurocognitive endophenotype for autism, because it is found in autism and in relatives of individuals with autism. Autistic traits can also extend continuously into the general population, raising the possibility that reduced adaptive coding of face identity may be more generally associated with autistic traits. In the present study, we investigated whether adaptive coding of face identity decreases as autistic traits increase in an undergraduate population. Methods Adaptive coding was measured using face identity aftereffects, and autistic traits were measured using the Autism-Spectrum Quotient (AQ) and its subscales. We also measured face and car recognition ability to determine whether autistic traits are selectively related to face recognition difficulties. Results We found that men who scored higher on levels of autistic traits related to social interaction had reduced adaptive coding of face identity. This result is consistent with the idea that atypical adaptive face-coding mechanisms are an endophenotype for autism. Autistic traits were also linked with face-selective recognition difficulties in men. However, there were some unexpected sex differences. In women, autistic traits were linked positively, rather than negatively, with adaptive coding of identity, and were unrelated to face-selective recognition difficulties. Discussion The sex differences observed here indicate that autistic traits can have different neurocognitive correlates in men and women and raise the intriguing possibility that endophenotypes of autism can differ in men and women. However, our results suggest that, at least for men, atypical adaptive coding of faces may be an endophenotype for autism. Acknowledgements This research was supported by the Australian Research Council Centre of Excellence in Cognition and its Disorders (CE110001021), an ARC Professorial Fellowship to Rhodes (DP0877379) and an ARC Discovery Outstanding Researcher Award to Rhodes (DP130102300). We thank Mayu Nishimura and Daphne Maurer for co-creating the Robbers Game used in the Identity Aftereffects task and Ainsley Read for assistance with testing. Ethical approval was granted by the Human Research Ethics Committee of the Univer Keywords: autistic traits, broader autism phenotype, face recognition, face identity aftereffects, adaptive coding, Endophenotypes Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Oral Topic: Other Citation: Rhodes G, Jeffery L, Taylor L and Ewing L (2013). Autistic traits and adaptive coding of face identity. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00193 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Oct 2013; Published Online: 25 Nov 2013. * Correspondence: Prof. Gillian Rhodes, ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia, perth, Australia, gillian.rhodes@uwa.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Gillian Rhodes Linda Jeffery Libby Taylor Louise Ewing Google Gillian Rhodes Linda Jeffery Libby Taylor Louise Ewing Google Scholar Gillian Rhodes Linda Jeffery Libby Taylor Louise Ewing PubMed Gillian Rhodes Linda Jeffery Libby Taylor Louise Ewing Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.