The development trend of evaluating face-recognition technology

Facial recognition is a feature that uses facial detection algorithms to detect a face and then invokes facial recognition algorithms to try to match the person’s face. First, a person’s face needs to be enrolled; during enrollment, the person’s facial details are saved in a database—in this case, a mobile phone. The facial recognition algorithm uses this database to match the currently presented face with the faces saved in the database. The efficiency of a facial recognition algorithm depends upon the speed at which it can detect and recognize faces. The problem we address in this paper is that a reliable, automated method for testing facial recognition features in mobile phones. Because the multimedia capabilities of smartphones have expanded phenomenally, the need for thoroughly testing facial recognition algorithms has become crucial. The uses of facial recognition can range from facial authentication for unlocking phones to security for a number of other applications. To meet these needs, a reliable, automated test for validating the facial recognition algorithms must be developed. The challenge for the software test team was to automate the test cases, which involve tilting the phone at specific angles from the test subject. The permissible angular movements of the phone are determined by the algorithmic specifications of the facial recognition algorithm. We tested scenarios involving multiple faces, motion blur, panning the phone in front of the test subject faces at various speeds, and so on. We adopted a robotic arm to perform the facial recognition test cases and developed software to program the robotic arm to test the phone’s facial recognition software for functionality, performance, and stability as well as adversarial tests. This paper discusses the computer vision use case for facial recognition and describes how we developed an automated facial recognition test suite using a robotic arm.

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance “bar” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields “state-of-the-art” performance levels on a standard face recognition set (“labeled faces in the wild”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more “realistic” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.

This research sought to examine differences in the cross-modal facilitation effect of voice on facial identity recognition under mask occlusion for both oneself and others. Employing a facial recognition paradigm, we examined the influence of voice on facial identity recognition under static and dynamic mask occlusion through two eye-tracking experiments. The behavioral results from Experiments 1 and 2 indicate that mask occlusion interfered with recognition for both static and dynamic faces, with greater interference observed for others' faces than for self-faces. In addition, voice exerts cross-modal enhancement effects on faces, with greater enhancement observed for masked faces than for no mask. Furthermore, voice provides stronger enhancement for others' dynamic faces than for their self-dynamic faces. Eye-tracking data from both experiments revealed that the difference in dynamic facial recognition between self-faces and others' faces due to voice emerged in the early stages of dynamic facial recognition and persisted into later stages. However, regardless of whether they were in the early or late stages of static facial recognition, the facilitation effect of voice did not differ between themselves and others. This study revealed that the cross-modal facilitation of visual stimuli by voice is influenced by the self-advantage effect.

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

Face recognition is widely used as identity authentication method in the Internet of Things (IoTs). However, compared with static camera, the background of IoT devices may change. Additionally, the facial expression or posture may change as well. Although face recognition has been researched for several decades, it is still a challenging task to design a robust face recognition system in IoT environment. This paper designs a special face recognition system by introducing deep transfer learning to adapt difference scenes in IoT environment. The face recognition system consists of two parts: terminal smart devices and cloud center. The terminal smart device consists of a camera to capture face picture, a wireless network part to communicate with cloud center, and a screen to show recognition result. The cloud center receives the face picture from terminal smart device and returns the face recognition result by using an offline deep transfer learning model. Compared with previous models, deep transfer learning model can better adapt different scenes. The experiments shows that the proposed face recognition system achieves more promising results than two source‐only models and two shallow transfer learning models.

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

In recent years, technologies related to face recognition have achieved rapid development. Face recognition Algorithms using Convolutional Neural Network (CNN) in the field of deep learning have been improved compared with traditional ones in terms of recognition accuracy, anti-interference ability and the speediness of identification. The mature face recognition technology now plays a decisive role in many areas, and also one of the key technologies in the facilitation of unmanned supermarkets. However, most algorithms nowadays just use the face database in unrestricted scenes to train, verify and evaluate. Their performance in practical applications is not satisfactory enough. In this paper, we eager to find a face recognition algorithm and system that are more suitable for unmanned supermarket scenes. After in-depth analysis of the scene, we find out that motion blur and large-scale recognition are two important reasons why current algorithms do not perform well. Aim to solve these problems, we summarize and propose new algorithms based on the existing ones. The experimental results show that the algorithm has effective face recognition ability in unmanned supermarket.

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many machine learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC) $^{2}\text{A}$ and Block FLDA.

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.

Face recognition technology has evolved as a popular identification technique to perform verification of human identity. By using the feature extraction methods and dimensionality reduction techniques in the pattern recognition applications, a number of facial recognition systems has been produced with distinct measure of success. Various face recognition algorithms and their extensions, have been proposed in the past three decades. However, face recognition faces challenging problems in real life applications because of the variation in the illumination of the face images. In the recent years, the research is focused towards Illumination-invariant face recognition system and many approaches have been proposed. But, there are several issues in face recognition across illumination variation which still remains unsolved. This paper provides a research on an efficient illumination-invariant face recognition system using Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). For processing the illumination invariant image, low frequency components of DCT are used to normalize the illuminated image, odd and even components of DCT is used for compensation in illumination variation and PCA is used for recognition of face images. The existing approaches in illumination Invariant face recognition are comprehensively reviewed and discussed. The proposed approach is validated with Yale Face Database B. Experimental results demonstrate the effectiveness of this approach in the performance of face recognition.

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

The last decade has seen automatic face recognition evolve from small-scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve verification accuracies comparable to those of fingerprint recognizers. In these experiments, only frontal face images taken under controlled lighting conditions were used. As the use of face recognition systems expands toward less restricted environments, the development of algorithms for view and illumination invariant face recognition becomes important. However, the performance of current algorithms degrades significantly when tested across pose and illumination, as documented in a number of evaluations. In this chapter, we review previously proposed algorithms for pose and illumination invariant face recognition. We then describe in detail two successful appearance-based algorithms for face recognition across pose, eigen light-fields, and Bayesian face subregions. We furthermore show how both of these algorithms can be extended toward face recognition across pose and illumination.

Laterality effects in normal subjects' recognition of familiar faces, voices and names. Perceptual and representational components

In this paper, it studies two critical technical links of face detection and face recognition in the recognition of face, and conducts analyses and comparisons on some commonly used face detections and recognition algorithms. Through the collation of this information and the combination of related knowledge in the digital image processing, it proposes a face recognition algorithm based on the singular value decomposition. After preprocessing face images, by the use of the projection method, it obtains positions of five sense organs, and then extracts local feature values near five sense organs as the main features of faces by using the singular value decomposition. For different face images of the same individual, the matching degree of feature values will be very high.KeywordsFace recognitionimage recognitionlocal informationfeature extraction