Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval

Speech Processing in Computer Vision Applications

Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

Face Recognition

Mirzaei, M. E. (2020). Review of six popular method: Face Recognition. Iberian Journal of Applied Sciences and Innovations, 1(1).Simulation of human ability has always been attractive topic for researchers but had often been limited to hardware part. With the advancement of technology and emerge of deep learning and machine learning, hope to simulate human perception increased. Face recognition is one of the most important capability of human perception that use in routine. Many researches have been done that led to proposing different approach and various methods. This paper is a survey of face recognition methods that proposed in past decades and also categorize them in meaningful approachespublishersversionpublishe

Design and Real-World Application of Novel Machine Learning Techniques for Improving Face Recognition Algorithms

Recent progress in machine learning has made possible the development of real-world face recognition applications that can match face images as good as or better than humans. However, several challenges remain unsolved. In this PhD thesis, some of these challenges are studied and novel machine learning techniques to improve the performance of real-world face recognition applications are proposed. Current face recognition algorithms based on deep learning techniques are able to achieve outstanding accuracy when dealing with face images taken in unconstrained environments. However, training these algorithms is often costly due to the very large datasets and the high computational resources needed. On the other hand, traditional methods for face recognition are better suited when these requirements cannot be satisfied. This PhD thesis presents new techniques for both traditional and deep learning methods. In particular, a novel traditional face recognition method that combines texture and shape features together with subspace representation techniques is first presented. The proposed method is lightweight and can be trained quickly with small datasets. This method is used for matching face images scanned from identity documents against face images stored in the biometric chip of such documents. Next, two new techniques to increase the performance of face recognition methods based on convolutional neural networks are presented. Specifically, a novel training strategy that increases face recognition accuracy when dealing with face images presenting occlusions, and a new loss function that improves the performance of the triplet loss function are proposed. Finally, the problem of collecting large face datasets is considered, and a novel method based on generative adversarial networks to synthesize both face images of existing subjects in a dataset and face images of new subjects is proposed. The accuracy of existing face recognition algorithms can be increased by training with datasets augmented with the synthetic face images generated by the proposed method. In addition to the main contributions, this thesis provides a comprehensive literature review of face recognition methods and their evolution over the years. A significant amount of the work presented in this PhD thesis is the outcome of a 3-year-long research project partially funded by Innovate UK as part of a Knowledge Transfer Partnership between University of Hertfordshire and IDscan Biometrics Ltd (partnership number: 009547)

A Review on Face Anti-Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types