An Effective Approach Towards Content-Based Human Facial Image Detection and Retrieval

The difficulties of locating a desired facial image in a large and varied collection are now the current main problem in this field. In order to search in such a large and varied images’ collection, there is a growing need for efficient storage and retrieval techniques. In this research, an effective approach towards content-based human facial image detection and retrieval is proposed. The research proposes a technique of face segmentation based on which a new method of features extraction from the human face is developed. The capability and effectiveness of the RGB, HSV, and HSI color space models on facial image retrieval technique are investigated. The effect of the vectors dimension of the eigenfaces features and the color histogram features, and the distribution values of the color space coordinates on facial image retrieval are studied. Viola-Jones face detection method is employed to obtain the location, extent and dimensions of each face. Moreover, for the measurement of distance and classification purposes, Euclidean distance is utilized. Several experiments based on precision and recall approach were conducted to evaluate the proposed methods. The retrieval result of the facial image given by the proposed method showed excellent improvement comparing to those achieved when using the traditional method of visual features extraction.