Most face recognition systems focus on photo-based face recognition. In this paper, we present a face recognition system based on face sketches. The proposed system contains two elements: pseudo-sketch synthesis and sketch recognition. The pseudo-sketch generation method is based on local linear preserving of geometry between photo and sketch images, which is inspired by the idea of locally linear embedding. The nonlinear discriminate analysis is used to recognize the probe sketch from the synthesized pseudo-sketches. Experimental results on over 600 photo-sketch pairs show that the performance of the proposed method is encouraging.

In this paper, we propose a realtime face detection approach based on local structure and texture of the objects in gray-level images. Our strategy is to map the local spatial structures and image textures of face class into binary patterns, and use these binary patterns as local descriptors. Boosting based face detector is constructed using these local descriptors, and cascade scheme is employed to further improve the efficiency of the face detector. Compared to the existing face detection approaches, our proposed method has two advantages: (1) it is robust to illumination changes to some extend, for the features use the information of local relationship instead of the original gray values; (2) the computational cost is very low, both in training procedure and evaluation step. The experimental results show that the proposed method can meet the demand of realtime applications with a satisfied detection performance.

In this paper, we present a novel face detection approach using improved local binary patterns (ILBP) as facial representation. ILBP feature is an improvement of LBP feature that considers both local shape and texture information instead of raw grayscale information and it is robust to illumination variation. We model the face and non-face class using multivariable Gaussian model and classify them under Bayesian framework. Extensive experiments show that the proposed method has an encouraging performance.

An effective replay detection method for broadcasting sports videos is proposed in this paper. The algorithm is composed of logos detection and replay recognition. Firstly, a logo-template is automatically extracted from logo-transition at the beginning of a video. It is further used to detect the other logos in the same video. Then, with taking logos as boundaries, the video can be divided into replay and non-replay segments. To identify replay segments, an SVM classifier with mid-level features is utilized. Experiments demonstrate the effectiveness of this method.