Kwok-Wai Wong

An efficient algorithm for human face detection and facial feature extraction under different conditions

Abstract In this paper, an efficient algorithm for human face detection and facial feature extraction is devised. Firstly, the location of the face regions is detected using the genetic algorithm and the eigenface technique. The genetic algorithm is applied to search for possible face regions in an image, while the eigenface technique is used to determine the fitness of the regions. As the genetic algorithm is computationally intensive, the searching space is reduced and limited to the eye regions so that the required timing is greatly reduced. Possible face candidates are then further verified by measuring their symmetries and determining the existence of the different facial features. Furthermore, in order to improve the level of detection reliability in our approach, the lighting effect and orientation of the faces are considered and solved.

A robust scheme for live detection of human faces in color images

In this paper, an efficient algorithm for detecting human faces in color images is proposed. The first step of our algorithm is to segment the possible skin-like regions in an image by using color information. One of the major problems of using skin color is that a face region may not be detected under poor lighting conditions, or if the lighting conditions vary over the face region. Our approach considers the distributions of the color components of skin pixels under different illuminations. This information can be used to identify skin-color pixels reliably under varying lighting conditions. The skin-color regions are then clustered and verified as human face regions. In order to improve the reliability and accuracy, an eigenmask that has a large magnitude at the important facial features of a human face is used in the detection. Experimental results show that this algorithm can detect human faces under varying lighting conditions reliably and fast.

Simplified Gabor wavelets for human face recognition

Gabor wavelets (GWs) are commonly used for extracting local features for various applications such as object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGW features for face recognition. Experimental results show that using SGWs can achieve a performance level similar to using GWs, while the runtime for feature extraction using SGWs is, at most, 4.39 times faster than that of GWs implemented by using the fast Fourier transform (FFT).

An efficient low bit-rate video-coding algorithm focusing on moving regions

Block-based motion estimation and compensation are the most popular techniques for video coding. However, as the shape and the structure of an object in a picture are arbitrary, the performance of such conventional block-based methods may not be satisfactory. In this paper, a very low bit-rate video coding algorithm that focuses on moving regions is proposed. The aim is to improve the coding performance, which gives better subjective and objective quality than that of the conventional coding methods at the same bit rate. Eight patterns are pre-defined to approximate the moving regions in a macroblock. The patterns are then used for motion estimation and compensation to reduce the prediction errors. Furthermore, in order to increase the compression performance, the residual errors of a macroblock are rearranged into a block with no significant increase of high-order DCT coefficients. As a result, both the prediction efficiency and the compression efficiency are improved.

A reliable approach for human face detection using genetic algorithm

In this paper, a reliable method for detecting human faces in an image is devised. The approach is based on the genetic algorithm and the eigenface technique. As the genetic algorithm is a computationally intensive process, the searching space for possible face regions is limited to possible eye regions so that the required timing is greatly reduced. In addition, the lighting effect and orientation of the faces are considered and solved in this method.

An efficient color compensation scheme for skin color segmentation

Skin color is a useful means for human face detection. In this paper, we propose an efficient color compensation method for skin color segmentation under varying lighting conditions. One of the major problems of using skin color is that a face region may not be detected under poor or uneven lighting conditions. Our approach considers the distribution of the color components of skin pixels and the color response of capturing machines under different illuminations. Experimental results show that this algorithm can improve the performance of face segmentation under poor or strong lighting conditions.

Efficient color face detection algorithm under different lighting conditions

We present an efficient and reliable algorithm to detect human faces in an image under different lighting conditions. In our algorithm, skin-colored pixels are identified using a region-based approach, which can provide more reliable skin color segmentation under various lighting conditions. In addition, to compensate for ex- treme lighting conditions, a color compensation scheme is pro- posed, and the distributions of the skin-color components under various illuminations are modeled by means of the maximum- likelihood method. With the skin-color regions detected, a ratio method is proposed to determine the possible positions of the eyes in the image. Two eye candidates form a possible face region, which is then verified as a face or not by means of a two-stage procedure with an eigenmask. Finally, the face boundary region of a face can- didate is further verified by a probabilistic approach to reduce the chance of false alarms. Experimental results based on the HHI MPEG-7 face database, the AR face database, and the CMU pose, illumination, and expression (PIE) database show that this face de- tection algorithm is efficient and reliable under different lighting con- ditions and facial expressions.

Feature-aging for age-invariant face recognition

Age-invariant face recognition has attracted some recent attention. In real applications, the age progression of those face images, stored in a face database for recognition and identification purposes, should also be considered, so as to achieve a higher accuracy level. In this paper, we propose a method to predict the aging of facial features so as to alleviate the effect of age progression on face recognition. The original facial feature and the aged facial feature of a face image should be correlated, so they are fused by using canonical correlation analysis to form a coherent feature for face recognition. The performance of our proposed approach is evaluated based on the FGNet database, and compared to some existing face recognition algorithms. Experiment results show that our proposed method can achieve a superior performance, when the query and probe face images have a large age difference.

Simplified gabor wavelets for efficient feature extraction

Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).

A robust algorithm for detection of human faces in color images

An efficient algorithm for detecting human faces in color images is proposed. The first step of our algorithm is to segment the possible skin-like regions in an image by using color information. One of the major problems of using skin color is that a face region may not be detected under poor or intense lighting conditions, or if the lighting conditions vary over the face region. Our approach considers the distribution of the color components of skin pixels under different illumination. This information can be used to identify skin color pixels reliably under different lighting conditions. The skin color regions are then clustered and verified as human face regions or not. In order to improve the reliability of detection, an eigenmask that has a large magnitude at the important facial features of a human face is devised. Experimental results show that this algorithm can detect human faces under different lighting conditions reliably.