This paper proposes a scale invariant face detection method which combines higher-order local autocorrela-tion (HLAC) features extracted from a log-polar transformed image with Linear Discriminant Analysis for \face" and \not face" classiication. Since HLAC features of log-polar image are sensitive to shifts of a face, we utilize this property and develop a face detection method. HLAC features ...

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) Applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component...

On single sample condition, current face recognition algorithm yields serious performance drop or even fail to work, therefore a face recognition algorithm based on LGBP and locality preserving discriminant analysis (LPDA) was proposed. Image enhancement and geometric transformation were firstly introduced to single sample face image for face reconstruction, making reconstruct image and origina...

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the inpu...

This paper presents an algorithm which detects automatically the feature points in a face image. This is a fundamental task in many applications, in particular in an automatic face recognition system. Starting from a frontal face image with a plain background we have effected an image segmentation to detect the different facial components (eyebrow, eyes, nose, mouth and chin). After this we hav...

A coding method for face images by segmenting face regions such as eyes, mouth, etc. is introduced. Each region is removed from the face image, leaving a smoothly interpolated background which is suitable for e5cient compression. Extracted region images are coded using vector quantization. The overall method is found to achieve comparable compression ratios with compatible image coding methods....

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which m...

Most of 2DPCA-enhanced approaches improve face recognition rate while at the expense of computation load. In this paper, an approach is proposed to greatly improve face recognition rate with slightly increased computation load. In this approach, the 2DPCA is applied against a face image to extract important image features for selection. A weight is then assigned to each of selected image featur...

In this paper, we propose a novel method for face recognition which uses the polar coordinate system instead of the conventional cartesian coordinate system. Among the central area of a face, we select a point as a pole and make a polar image of a face by evenly sampling pixels in each direction of 360 degrees around the pole. The polar coordinate system delineates near-pole area more vividly t...