In recent years, developing Surveillance Systems (SS) for security has been one of the most active research fields in most applications. These systems used to adjust, enhance, and improve the security. One of these systems, face recognition system plays an efficient and very important tool for several applications despite the existence of different surveillance systems, like hand geometry, iris scan, as well as fingerprints. This is because it is natural, non-intrusive, and inexpensive. For the past two decades, various face recognition methods have been proposed to reduce the amount of calculation and improve the recognition rate. These proposed methods could be categorized into three significant categories: Local feature approaches, Subspace learning approaches, and Correlation filters approaches. In this paper, we discuss and compare some common face recognition algorithms. Our objective of this work is to demonstrate the effectiveness and feasibility of the best methods for face recognition in terms of design, implementation, and application.
We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user’s trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.