IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 117 EFFECTIVE FACE FEATURE FOR HUMAN IDENTIFICATION S. Adebayo Daramola 1 , Tiwalade Odu 2 , Olujimi Ajayi 3 1 Senior Lecturer, Department of Electrical and Information Engineering, Covenant University Ota, Ogun State, Nigeria 2 Assistant Lecturer, Department of Electrical & Information Engineering, Covenant University Ota, Ogun State, Nigeria 3 Post Graduate Student, Department of Electrical & Information Engineering, Covenant University Ota, Ogun State, Nigeria Abstract Face image is one of the most important parts of human body. It is easily use for identification process. People naturally identify one another through face images. Due to increase rate of insecurity in our society, accurate machine based face recognition systems are needed to detect impersonators. Face recognition systems comprise of face detector module, preprocessing unit, feature extraction subsystem and classification stage. Robust feature extraction algorithm plays major role in determining the accuracy of intelligent systems that involves image processing analysis. In this paper, pose invariant feature is extracted from human faces. The proposed feature extraction method involves decomposition of captured face image into four sub-bands using Haar wavelet transform thereafter shape and texture features are extracted from approximation and detailed bands respectively. The pose invariant feature vector is computed by fusing the extracted features. Effectiveness of the feature vector in terms of intra-person variation and inter-persons variation was obtained from feature plots. Keywords: Center points, Edge detected image, Feature Face-image, Pose invariant. ----------------------------------------------------------------------***------------------------------------------------------------------------ 1. INTRODUCTION Recognition of people is usually done using widely accepted biometric traits like signature, fingerprint and face. Face identification can be done naturally by people or artificially using intelligent machines. Naturally people find it easy to identify faces that are well-known compare to strange faces. Machine based face recognition involves capturing of face images using digital camera under variable facial expression thereafter captured face images are sent to system for identification. Face images contain important revealing parts like forehead, eyes, nose, mouth and chin. These parts occupy different locations and vary closely from one person to another. The major challenge that makes automatic face identification difficult is high intra variation within face images of the same person. This intra variation is caused majorly by variable illumination and pose. Face recognition systems comprise of face detection, face image preprocessing, feature extraction, training and matching. Feature extraction subsystem is mainly considered in this work. At preprocessing level many morphological processing are done to normalize illumination effect [1]. One method of reducing effect of pose variation is by using robust feature as input data to classification algorithm. Extraction of feature from face images can be done in many ways. In the past many researchers have used methods that involves all the pixels of the whole image [2][3]. On many occasions feature are extracted from vital parts of face images [4][5]. Also face images may be decomposed into smaller image blocks before feature extraction is carried out [6][7][8]. Extraction of feature vector was carried out using group of pixel values within eyes, lip and nose regions in [9]. The feature vector size was reduced and further processed by application of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In [10], face feature extraction was done using three multi-scale representation techniques based on Gabor filter, log Gabor filter and Discrete Wavelet Transform whereas in [11], invariant face feature was extracted for recognition purpose using Haar wavelet transform and Principal Component Analysis . In this work new approach for feature extraction different from those used in previous works is introduced. The proposed feature will suppress effect of varying facial expression. This is achieved by extracting local shape features from smaller image blocks. And this feature is fused with texture feature from detailed bands. The rest of the paper is organized as follows: Section 2 describes the collection of input images and decomposition. Section 3 introduces the new feature extraction method, and section 4 describes feature plot result. Finally, conclusion is presented in section 5. 2. INPUT FACE IMAGES Digital camera was use to capture face images of people under variable illumination and pose conditions. Fig.1 shows set of face image obtained as the input image to the proposed feature extraction algorithm.