Face Detection Results Research Articles

ROTATION INVARIANT MULTIVIEW FACE DETECTION USING SKIN COLOR REGRESSIVE MODEL AND SUPPORT VECTOR REGRESSION

In this paper, an automatic rotation invariant multiview face detection method, which utilizes modified Skin Color Model (SCM), is presented. First, Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) based hybrid models are used to classify human skin regions from color images. The novelty of the adaptive hybrid model is its ability to predict the chromatic skin color band for individual images based on calibration differences of camera and luminance condition of environment. Classified skin regions are then converted to gray scale image with a threshold based on the predicted chromatic skin color bands, which further enhances detection performance. Next, Principle Component Analysis (PCA) is applied to gray segmented regions. Face detection is carried out based on the PCA-based extracted features, along with selected features, using support vector regression. The output of this procedure is used to report the final result of face detection. The proposed method is also beneficial for the rotation invariant face recognition problem.

T-eigenfaces selection for false face reduction

We have developed a false face reduction algorithm dedicated for face detection in an unconstrained environment, based on the discriminative facial T-shape region constructed by human eyes, nose, and mouth. The algorithm started with a detected face, through lighting compensation, normalization, facial T-shape region extraction, and eigenface selection using genetic algorithms to classify face/false face robustly. Results of face detection with and without the proposed false face reducer show substantial improvements on precision rate at a little loss of recall rate.

Real-Time Iris Detection on Coronal-Axis-Rotated Faces

Real-time face and iris detection on video sequences is important in diverse applications such as, study of the eye function, drowsiness detection, virtual keyboard interfaces, face recognition, and multimedia retrieval. In this paper, a real-time robust method is developed to detect irises on faces with coronal axis rotation within the normal range of to . The method allows head movements with no restrictions to the background. The method is based on anthropometric templates applied to detect the face and eyes. The templates use key features of the face such as the elliptical shape, and location of the eyebrows, nose, and lips. For iris detection, a template following the iris-sclera boundary shape is used. The method was compared to Maio-Maltoni's and Rowley's methods for face detection on five video sequences (TEST 1). The method was also assessed in an additional set of five video sequences for iris detection (TEST 2). Results of correct face detection in TEST 1 were above 99% in three of the five video sequences. The fourth video sequence reached 97.6% and the third 90.6%. In TEST 2, the iris detection was above 96% in all five video sequences with two above 99.7% and two at 100%. Face size estimation is also above 99.9%. The average processing time of our method was 0.02 s per frame. Thus, the proposed method can process frames at a rate near to 50 frames/s, and therefore, is applicable in real time in a standard personal computer (PC 1.8 GHz).