A new approach to face detection based on YCgCr color model and improved AdaBoost algorithm

Human face detection plays considerably important role in various biometric applications like crowd surveillance, photography, human-computer interaction, tracking, automatic target recognition, artificial intelligence and various security applications. Varying illumination conditions, color variance, brightness, pose variations are major challenging problems for facial detection. Skin color based segmentation and AdaBoost based facial detection scheme are the two most widely used techniques for face detection. But skin color segmentation method has very high false positive detection rate in images with complicated background and AdaBoost algorithm does not provide desired results for detecting images having multiple pose and multiple faces. Apart from this, AdaBoost approach has higher accuracy, but slower speed and skin color segmentation method has a faster speed of detection, but lower accuracy; and. So our paper proposes a novel facial detection scheme based on the integration of YCgCr based skin color segmentation and improved AdaBoost algorithm. Also morphological operators are applied to improve the detection performance. From the experimental results, it can be deduced that the proposed face detection algorithm improves the detection speed, accuracy and capable of real time face detection. Simulation results are used to show that our proposed method achieves accuracy of approximately 97% and has considerably good performance on images having complex background and can detect faces of various sizes, postures and expressions, under uncontrolled lighting environments.