Multiple Face Recognition Research Articles

A Comprehensive Analysis of Algorithms in Multiple-Face Recognition

A Comprehensive Analysis of Algorithms in Multiple-Face Recognition

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.

JAMsFace: joint adaptive margins loss for deep face recognition

Deep feature learning has become crucial in large-scale face recognition, and margin-based loss functions have demonstrated impressive success in this field. These methods aim to enhance the discriminative power of the softmax loss by increasing the feature margin between different classes. These methods assume class balance, where a fixed margin is sufficient to squeeze intra-class variation equally. However, real-face datasets often exhibit imbalanced classes, where the fixed margin is suboptimal, limiting the discriminative power and generalizability of the face recognition model. Furthermore, margin-based approaches typically focus on enhancing discrimination either in the angle or cosine space, emphasizing one boundary while disregarding the other. To overcome these limitations, we propose a joint adaptive margins loss function (JAMsFace) that learns class-related margins for both angular and cosine spaces. This approach allows adaptive margin penalties to adjust adaptively for different classes. We explain and analyze the proposed JAMsFace geometrically and present comprehensive experiments on multiple face recognition benchmarks. The results show that JAMsFace outperforms existing face recognition losses in mainstream face recognition tasks. Specifically, JAMsFace advances the state-of-the-art face recognition performance on LFW, CPLFW, and CFP-FP and achieves comparable results on CALFW and AgeDB-30. Furthermore, for the challenging IJB-B and IJB-C benchmarks, JAMsFace achieves impressive true acceptance rates (TARs) of 89.09% and 91.81% at a false acceptance rate (FAR) of 1e-4, respectively.

The contribution of different face parts to deep face recognition

The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.

Real-time edge computing on multi-processes and multi-threading architectures for deep learning applications

As the computing power of embedded system hardware devices continues to grow, more and more deep learning models have been gradually transplanted into edge devices. Accordingly, a variety of application scenarios have been developed with more complex inference models or multiple models that work together. Moreover, with consideration given to cost, a single edge computing device can integrate multiple input sources and simultaneously complete the application of multiple scenarios. To achieve good performance on edge computing devices, this paper probes into the software architecture design of multi-processes and multi-threading architectures on edge computing device, in order to realize real-time edge computing. In the experiment, multiple face-related deep learning models, namely, face detection, face recognition, age estimation, gender estimation, and emotion estimation, are used to demonstrate the differences between multi-processes and multi-threading on edge computing devices. According to the experimental results, it is known that if the number of central processing unit (CPU) cores and memory space are small, the multi-threading architecture can better improve efficiency; conversely, the multi-processes architecture can be used. The architecture proposed in this paper has reference value, and can improve the execution efficiency of deep learning technology on edge computing, and reduce the cost of deployment.

Privacy protection generalization with adversarial fusion.

Several biometric privacy-enhancing techniques have been appraised to protect face image privacy. However, a face privacy protection algorithm is usually designed for a specific face recognition algorithm. When the structure or threshold of the face recognition algorithm is fine-tuned, the protection algorithm may be invalid. It will cause the network bloated and make the image distortion target multiple FRAs through the existing technology simultaneously. To address this problem, a fusion technology is developed to cope with the changeable face recognition algorithms via an image perturbation method. The image perturbation is performed by using a GAN-improved algorithm including generator, nozzles and validator, referred to as the Adversarial Fusion algorithm. A nozzle structure is proposed to replace the discriminator. Paralleling multiple face recognition algorithms on the nozzle can improve the compatibility of the generated image. Next, a validator is added to the training network, which takes part in the inverse back coupling of the generator. This component can make the generated graphics have no impact on human vision. Furthermore, the group hunting theory is quoted to make the network stable and up to 4.8 times faster than other models in training. The experimental results show that the Adversarial Fusion algorithm can not only change the image feature distribution by over 42% but also deal with at least 5 commercial face recognition algorithms at the same time.

다수의 얼굴 인식을 통한 자동출석체크 시스템

Non-contact biometric identification technology is once again receiving social attention these days when untact technology is required due to the Covid-19. Face recognition technology is being used in a variety of platforms such as airport immigration systems, hospital patient management systems, financial self-authentication systems, criminal suspect detection systems, and personal authentication systems for mobile devices, and is showing its effectiveness. In this paper, multiple face recognition technology is applied to the automatic attendance system. Unlike when only one person is recognized, multiple face recognition results in lower accuracy and slower processing speed. In this paper, a frontal face detection algorithm is proposed. Using the frontal face detection algorithm, only the frontal face was used for recognition, and when 36 people were recognized, 97.2% accuracy was achieved. In addition, we proposed an erasing multiple face recognition method and increased the average number of frames processed per second as the recognition completion rate increased.

Multiple Human Face Detection and Recognition based on LBPH and Machine Learning

Multiple Human Face Detection and Recognition based on LBPH and Machine Learning

Self-Construal Priming Modulates Ensemble Perception of Multiple-Face Identities.

This study explored the modulatory role of independent/interdependent self-construal on ensemble perception. Two experiments were conducted to study the effect of self-construal on ensemble coding of multiple-face identities (Experiment 1) and dot size (Experiment 2) separately. Before the implicit ensemble perception task, participants in both experiments were either primed with independent or interdependent self-construal via a well-validated pronoun circle task, in which they were exposed to either singular (“I,” “me,” and “my”) or plural (“We,” “us,” and “our”) pronouns in essays. The results showed that interdependent self-construal (vs. independent self-construal) featured as global processing and emphasizing interconnectedness with others enhanced the ensemble coding of high-level features (e.g., identity in Experiment 1) but not of low-level features (e.g., size in Experiment 2). To the best of our knowledge, this study was the first to investigate the role of self-construal on ensemble representations. In sum, the results of the current study supported the domain-specific mechanism of ensemble perception on one hand, and extended the effect of self-construal on single face recognition to multiple face recognition on the other hand.

Face alignment: improving the accuracy of fast models using domain‐specific unlabelled data and a teacher–student scheme

Face alignment is a crucial step in multiple face analysis and recognition tasks. The current state-of-the-art is comprised by very slow methods based on deep learning that require computationally heavy inference and very fast methods based on cascades of regressors that lack the ability to cope with complicated cases or extreme poses. The authors show how collecting a small subset of unlabelled domain-specific data can improve the accuracy of fast-inference models utilising data annotated by a slower one and a teacher–student architecture. In the proposed solution, they annotate a small subset of facial images belonging to two challenging domains using a slow but more accurate model, and this data is used to incrementally train a fast one. Their results show that by adding as little as a 5% of challenging data, they can reduce the error rate in a specific domain up to 30% without losing any generalisation abilities. This training scheme has applicability in numerous computer vision and engineering problems where computational power and model size are constrained by the application and platform or real-time operation is a requirement.

Multiple histogram-based face recognition with high speed FPGA implementation

Face recognition is an algorithm that is capable of identifying or verifying a query face from multiple faces in the enrollment database. It poses a challenging problem in the field of image analysis and computer vision, especially for applications that deal with video sequences, face re-identification, or operate on intensity images and require fast processing. In this work, we introduce a high speed face recognition technique along with a high speed FPGA implementation. It uses a new similarity measure to estimate the distance between the query face and each of the database face images. The distance metric is the sum of the standard deviations between multiple histograms, which are calculated from each row of the query and database images. The lowest distance score refers to the database face that matches the query. The proposed technique is independent from the ambient illumination and outperforms the well-known face recognition algorithm “Eigenfaces” (it performs the face recognition 16× faster when both algorithms run on the same platform). Furthermore, we exploit data parallelism in our proposed algorithm to design a hardware accelerator and to implement it on an FPGA prototyping board. The results show 10x execution time improvement in comparison to the software version.

Multiple face tracking and recognition with identity-specific localized metric learning

While face recognition has been intensively studied in the literature, there are only a few attempts on visual recognition of multiple faces simultaneously in videos, which has potential applications in practical video surveillance. In this paper, we address the problem of visual recognition and tracking of multiple faces in real-world videos involving large pose variation and occlusion. Instead of recognizing individual face independently, we introduce the constraints of inter-frame temporal smoothness and within-frame identity exclusivity on multiple faces in videos, and model the tasks of multiple face recognition (MFR) and multiple face tracking (MFT) jointly in an alternative optimization framework. We show this joint formulation for two different tasks leads to significantly improved MFR accuracy. Specifically, as appearance matching for face instances over consecutive frames plays a critical role in MFT, we propose an identity-specific metric learning method with a part-based object representation to learn a localized transformation for each face subject in an online manner, under which face instances of the same subject over consecutive frames are pulled as close as possible, while those of different subjects are pushed far away. Empirically, we evaluate our method on several MFR sequences against baselines, and the results demonstrate that our method can achieve improved accuracy performance in various challenging recognition scenarios.

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

The main weakness of the k-Nearest Neighbor algorithm in face recognition is calculating the distance and sort all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved by utilizing the priority k-d tree search to speed up the process of k-NN classification. This paper proposes a method for student attendance systems in the classroom using facial recognition techniques by combining three levels of Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying the priority of k-d tree search to speed up the process of facial classification using k-Nearest Neighbor. The proposed algorithm is tested on two datasets that are Honda/UCSD video dataset and our dataset (AtmafaceDB dataset). This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor on our dataset is 95.56% with k = 5, whereas in the Honda / UCSD dataset it is only 82% with k = 3. The proposed method gives computational recognition time on our dataset 40 milliseconds.

Converted-face identification: using synthesized images to replace original images for recognition

The changes in appearance of faces, usually caused by pose, expression and illumination variations, increase data uncertainty in the task of face recognition. Insufficient training samples cannot provide abundant multi-view observations of a face. To address this issue, many pioneering works focus on generating virtual training images for better recognition performance. However, the issue also exists in a test set where a test image only conveys a split-second representation of a face and cannot cover more comprehensive features. In this paper, we propose a new face synthesis method for face recognition. In the proposed pipeline, we synthesize a virtual image using both the original image and its corresponding mirror one. Note that, we apply this technique both to the training and test sets. Then we use the newly generated training and test images to replace the original ones for face recognition. The aim is to increase the similarity between a test image and its corresponding intra-class training images. This proposed method is effective and computationally efficient. In order to verify this, we tested our system using multiple face recognition methods in terms of the recognition accuracy, based on either the synthesized images or original images. The methods used in the paper include statistical subspace learning algorithms and representation-based classification approaches. Experimental results obtained on FERET, ORL, GT, PIE and LFW show that the proposed approach improves the face recognition accuracy, especially on faces with left-right pose variations.

The hows and whys of face memory: level of construal influences the recognition of human faces.

Three experiments investigated the influence of level of construal (i.e., the interpretation of actions in terms of their meaning or their details) on different stages of face memory. We employed a standard multiple-face recognition paradigm, with half of the faces inverted at test. Construal level was manipulated prior to recognition (Experiment 1), during study (Experiment 2) or both (Experiment 3). The results support a general advantage for high-level construal over low-level construal at both study and at test, and suggest that matching processing style between study and recognition has no advantage. These experiments provide additional evidence in support of a link between semantic processing (i.e., construal) and visual (i.e., face) processing. We conclude with a discussion of implications for current theories relating to both construal and face processing.

Multi-view Face Recognition by Neural Network

Multiple views face recognition has become significant in various requisitions, such as observation, human workstation connection, and recreation. A reduction based feature extraction and neural network inspired by biological neurons for learning and recognizing the multiple views faces of the person has been presented in this paper. Neural Network (NN) is significant in the places where formulating an algorithmic solution is difficult and we need to retrieve the structure from existing and predefined data. Multi-view face recognition is required here because its more feasible and reliable than single view face recognition.

Multiple Face Recognition Using Local Features and Swarm Intelligence

Multiple Face Recognition Using Local Features and Swarm Intelligence

Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

Face Recognition Performance: Role of Demographic Information

This paper studies the influence of demographics on the performance of face recognition algorithms. The recognition accuracies of six different face recognition algorithms (three commercial, two nontrainable, and one trainable) are computed on a large scale gallery that is partitioned so that each partition consists entirely of specific demographic cohorts. Eight total cohorts are isolated based on gender (male and female), race/ethnicity (Black, White, and Hispanic), and age group (18–30, 30–50, and 50–70 years old). Experimental results demonstrate that both commercial and the nontrainable algorithms consistently have lower matching accuracies on the same cohorts (females, Blacks, and age group 18–30) than the remaining cohorts within their demographic. Additional experiments investigate the impact of the demographic distribution in the training set on the performance of a trainable face recognition algorithm. We show that the matching accuracy for race/ethnicity and age cohorts can be improved by training exclusively on that specific cohort. Operationally, this leads to a scenario, called dynamic face matcher selection, where multiple face recognition algorithms (each trained on different demographic cohorts) are available for a biometric system operator to select based on the demographic information extracted from a probe image. This procedure should lead to improved face recognition accuracy in many intelligence and law enforcement face recognition scenarios. Finally, we show that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.

Collaborative Face Recognition for Improved Face Annotation in Personal Photo Collections Shared on Online Social Networks

Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.