Video-based Side-view Face Recognition for Home Safety

In this paper, we introduce a registration method for side-view face recognition that is suitable for home safety applications. We use cameras attached at door posts, and recognize people as they pass through doors to estimate their location in the house. First, we present a new database that is collected using this setup, where we use side cameras and ambient light. We recorded videos of 14 people that pass through doors in 18 different paths. Next, we propose our recognition method where we automatically find the profile to register the face images. By applying hierarchical clustering we detect the frames that include falsely detected profiles and pose variations, and automatically remove them from the video sequence to improve our results. After registering, we find the nose tip, apply recognition based on profiles, and analyze our results

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

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

Robust Face Recognition System Based on a Multi-Views Face Database

In this chapter, we describe a new robust face recognition system base on a multi-views face database that derives some 3-D information from a set of face images. We attempt to build an approximately 3-D system for improving the performance of face recognition. Our objective is to provide a basic 3-D system for improving the performance of face recognition. The main goal of this vision system is 1) to minimize the hardware resources, 2) to obtain high success rates of identity verification, and 3) to cope with real-time constraints. Using the multi-views database, we address the problem of face recognition by evaluating the two methods PCA and ICA and comparing their relative performance. We explore the issues of subspace selection, algorithm comparison, and multi-views face recognition performance. In order to make full use of the multi-views property, we also propose a strategy of majority voting among the five views, which can improve the recognition rate. Experimental results show that ICA is a promising method among the many possible face recognition methods, and that the ICA algorithm with majority-voting is currently the best choice for our purposes

Performance Analysis of the ARIA Adaptive Media Processing Workflows using Colored Petri Nets

AbstractMultimedia systems are one of the most complex and interesting applications that are nowadays proposed to the users. Their complexity derives mainly from the fact that multimedia systems have to process huge amounts of data, while respecting real-time deadlines. For this reason performance evaluation of the underlaying workflow is a key issue in the design process of a new Multimedia system.In this paper we consider the ARchitecture for Interactive Arts (ARIA), an adaptive media processing workflow, developed at the Arizona State University, and outline a semi-automatic procedure to translate its specification into Colored Petri Nets. We then provide guidelines on how to compute the parameters for the performance models, and apply the proposed methodology to a realistic example of a face recognition application