Automatic face detection has been intensively studied for human-related recognition systems. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. In this paper, a new face detection algorithm is proposed. This speedy and robust solution developed, on the one hand is based on the segmentation of the color image to skin regions using a new approach to detect the pixels of the skin and the water shed segmentation method. On the other hand, using Gabor filters, combined with a proposed model of face, skin regions are classified into two classes: face and non-face. The integration of these tools in our algorithm permits to develop a face detector with very reasonable and efficient performances. Experimental results show that the method mentioned in this paper can achieve high detection rates and low false positives. To evaluate the detection speed of proposed algorithm, a comparison with a recent known algorithm is made too.

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In this paper we propose a faces recognition system. This system does not directly reproduce human vision on machine, but it seeks to find algorithms to achieve similar results by identifying a person using 2D image of his face. The descriptors used for features extraction, combine two algorithms: Principal Component Analysis (PCA) and a double Linear Discriminate Analysis (LDA) treatment. We chose the Support Vector Machine as an output classifier. Our approach has ensured a satisfactory recognition rate and a gain in terms of memory.

Head pose estimation has fascinated the research community due to its application in facial motion capture, human-computer interaction and video conferencing. It is a pre-requisite to gaze tracking, face recognition, and facial expression analysis. In this paper, we present a generic and robust method for model-based global 2D head pose estimation from single RGB Image. In our approach we use of the one part the Gabor filters to conceive a robust pose descriptor to illumination and facial expression variations, and that target the pose information. Moreover, we ensure the classification of these descriptors using a SVM classifier. The approach has proved effective view the rate for the correct pose estimations that we got.

Although human face recognition is a hard topic because of the multitude of parameters involved (e.g. variation in pose, illumination, facial expression, partial occlusions), it is very important to be interested and to invest in it viewed her many fields of application (identity authentication, physical and logical access control, video surveillance, human-machine interface...). The work presented in this paper is in this context. Its objective is the implementation of a complete architecture of a robust face recognition system. In a first time, a new approach has been developed for the detection of faces in a 2D color image. Secondly, is focused on the feature extraction using an original approach which includes the Gabor descriptor and a pose estimator. Finally, to validate this research, the developed system is tested on standard databases: Caltech_Web, AT&T and Color FERET.