Shengsheng Yu

Bio: Shengsheng Yu is an academic researcher. The author has contributed to research in topics: Eigenface & Face Recognition Grand Challenge. The author has an hindex of 1, co-authored 1 publications receiving 65 citations.

A survey of face detection, extraction and recognition

Abstract: The goal of this paper is to present a critical survey of existing lite- ratures on human face recognition over the last 4-5 years Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001 While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts As the number of proposed techniques increases, survey and evaluation becomes important

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Abstract: Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.

A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy

Abstract: As of late, the need for biometric security framework is elevated for giving safety and security against frauds, theft, and so on. Face recognition has gained a significant position among all biometric-based systems. It can be used for authentication and surveillance to prove the identity of a person and detect individuals, respectively. In this paper, a point-by-point outline of some imperative existing strategies which are accustomed to managing the issues of face recognition has been introduced along with their face recognition accuracy and the factors responsible to degrade the performance of the study. In the first section of this paper, different factors that degrade the facial recognition accuracy have been investigated like aging, pose variation, partial occlusion, illumination, facial expressions, and so on. While in the second section, different techniques have been discussed that worked to mitigate the effect of discussed factors.

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Abstract: Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.