Facial Recognition Dissertation Topics. - Research Prospect.
Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier.
Fig. 2: Face recognition building blocks. face recognition research, as CNNs are being used to solve many other computer vision tasks, such as object detection and recognition, segmentation, optical character recognition, facial expression analysis, age estimation, etc. Face recognition systems are usually composed of the following building blocks.
Face recognition is the practical branch of pattern recognition, which is aimed at the automatic localization of the face on a photo and if it is required at the identification of the person on the basis of her face. The function of face recognition is already used by a few corporations manufacturing IT products, personal computers and smart phones (for example, the face recognition software.
Abstract: Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research landscape of face recognition since 2014, launched by the breakthroughs of Deepface and DeepID methods.
Abstract. In this paper, the algorithm of face recognition technology is made a comprehensive study. Firstly studied the methods of face detection, facial feature of bottom-up approach, template matching method, the method of face appearance, and then focused on color-based face detection algorithm.
Abstract. An experiment was conducted on human face recognition performance in an access control scenario. Ten judges compared fifty individuals to security ID style photos where 20% of the photos were of different people, assessed to look similar to the individual presenting the photo.
Title: Face recognition using eigenfaces - Computer Vision and Pattern Recognit ion, 1991. Proceedings CVPR '91., IEEE Computer Society Confer Author.