Robotic Arm-Based Face Recognition Software Test Automation

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 practical application, the result of face recognition not only depends on the static face recognition algorithm, but also depends on the dynamic face recognition algorithm. In a face recognition system, face image acquisition equipment and algorithm processor hardware will also affect speed and effect of the recognition. Therefore, when evaluating face-recognition technology, we should not only carry out the static test of algorithm, but also carry out the dynamic face recognition test of actual faces. At the same time, considering the influence of hardware configuration, hardware configuration parameters of face recognition products or systems should be paid more attention. In the future, the development trend of evaluating face-recognition technology will become both static test in algorithm level and dynamic test of recognition effect to actual faces in application level should be carried out. Even the videotaped face-recognition test and system hardware configuration check should be carried out simultaneously.

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.

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.

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.

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.

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

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.

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.

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

Critical to any automated surveillance system is the recognition component. Since the inception of Pattern Recognition & Image processing, researchers across the globe continue to propose newer facial recognition algorithms. Each of these facial algorithms has its own pros and cons. A typical facial recognition algorithm consists of three steps namely: facial detection, feature extraction and recognition. In this paper we discuss serial and parallel implementation of four standard facial recognition algorithms namely, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) and FISHER Face. Work presented in this paper consists of two sections. In section 1, we discuss serial and parallel implementation of the algorithms. Section 2, presents a comparative performance analysis of the above 4 algorithms. A major challenge in this effort is the ability to explore and exploit concurrency in existing facial recognition algorithms to improve performance. Based on the algorithms performance, we have made few suggestions regarding the suitability of each of these algorithms for different domains/applications. All algorithms are implemented using open source OPENCV and OPENMP libraries.

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.

Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.

Chapter 10 - Local plastic surgery-based face recognition using convolutional neural networks

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.