Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

A biometric-based techniques emerge as the promising approach for most of the real-time applications including security systems, video surveillances, human-computer interaction and many more. Among all biométrie methods, face recognition offers more benefits as compared to others. Diagnosing human faces and localizing them in images or videos is the priori step of tracking and recognizing. But the performance of face detection is limited by certain factors namely lighting conditions, pose variation, occlusions, low resolution images and complex background. To overcome the problems, this paper examines a fusion strategy in the enhancement-based skin-color segmentation approach that can improve the performance of face detection algorithm. The method is robust against complex background, ethnicity and lighting variations. The method consists of three steps. The first step receives spatial transform techniques in parallel to enhance the contrast of the image, change the color space of the enhanced images to YCbCr, apply skin segmentation technique and yield the binary segmented images. The second step ascertains the weight of accuracy (WoA) of each of the segmented image and fed it into the fusion strategy to get the final skin detected region. Finally, the last step localizes the human face. The methodology is not constrained to just frontal face identification. However it is invariant with the diverse head postures, enlightment condition, and size of faces. The experimental result demonstrates the improvement in the accuracy and precision along with the reduction in FPR as compared to other enhancement classifiers.