Face detection is the first important step in many face image processing applications. Although a lot of work has been done on detecting frontal faces much less effort has been put into detecting faces with large image-plane or depth rotations. Most templates used in face detection are whole-face templates. However, such templates are ineffective for faces significantly rotated in depth. We pro...

Face detection in the wild needs to deal with various challenging conditions, which often leads to the situation where intraclass difference of faces exceeds interclass difference between faces and nonfaces. Based on this observation, in this paper we propose a locally rejected metric learning (LRML) based false positives filtering method. We firstly learn some prototype faces with affinity pro...

Automatic face recognition systems have made great strides in the past 10 years. They still, however, cannot cope with changes due to lighting and cannot detect disguises. Both of these issues are critical for the employment of face recognition systems in high security applications, such as embassy perimeters, federal plazas, and the like. We propose novel imaging solutions that address these d...

Measuring accuracy of a face detection algorithm hardly ever mentioned in detail and explicitly. The standard and terminology used in each experiment reported in each paper were vary and not standardized. In this paper, a method for calculating detection accuracy empirically is proposed. It consists of two algorithms that can be used for measuring accuracy of single face image and multiple face...

Face detection in still images and videos has been extensively studied over the last two decades. Attributed to the recent proliferation of cameras in consumer applications, research in face detection has gradually transformed into more unconstrained settings, with the goal of achieving performance close to humans. This presents two main challenges: (i) in addition to modeling the facial charac...

This paper describes a multi-stage approach for achieving fast and robust face detection. This approach was motivated by the work of Viola and Jones [7] using a cascade of classifiers yielding a coarse-to-fine strategy to significantly reduce detection time while maintaining high detection rate. However, it is distinguished from the previous work by two facts: (i) First, a new stage is added to...

In this paper, an unsupervised, automatic video human members(human body and parts) tracking algorithm is proposed based on a propabilictic human silhouette detection method and a geometric human silhouette analysis algorithm. First, the human silhouette is estimated using the following scheme : 1) an face detection, and 2) a human body detection based on biometric 2-D templates. Next, the huma...

Many potential applications exist where a fast and robust detection of human faces is required. Different cues can be used for this purpose. Since each cue has its own pros and cons we, in this paper, suggest to combine several complimentary cues in order to gain more robustness in face detection. Concretely, we apply skin-color, shape, and texture to build a robust detector. We define the face...

We 1 present a method for face detection which uses a new SVM structure trained in an expert manner in the eigenface space. This robust method has been introduced as a post processing step in a realtime face detection system. The principle is to train several parallel SVMs on subsets of some initial training set and then train a second layer SVM on the margins of the first layer of SVMs. This a...

This thesis analyzes the various modeling techniques for face recognition that are available to us within the eigenface framework and experiments with different methods that can be used to match faces using eigenfaces. It presents a probabilistic approach to matching faces and demonstrates it's superiority over other methods. It also carries out comprehensive parameter exploration experiments t...