Recent expansion in surveillance systems has motivated research in soft biometrics that enable the unconstrained recognition of human faces. Comparative soft biometrics show superior recognition performance than categorical soft biometrics and have been the focus of several studies which have highlighted their ability for recognition and retrieval in constrained and unconstrained environments. These studies, however, only addressed face recognition for retrieval using human generated attributes, posing a question about the feasibility of automatically generating comparative labels from facial images. In this paper, we propose an approach for the automatic comparative labelling of facial soft biometrics. Furthermore, we investigate unconstrained human face recognition using these comparative soft biometrics in a human labelled gallery (and vice versa). Using a subset from the LFW dataset, our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes.

This paper, presents a new Speed Detection Camera System (SDCS) that is applicable as a radar alternative. SDCS uses several image processing techniques on video stream in online -captured from single camera- or offline mode, which makes SDCS capable of calculating the speed of moving objects avoiding the traditional radars' problems. SDCS offers an en-expensive alternative to traditional radars with the same accuracy or even better. SDCS processes can be divided into four successive phases; first phase is Objects detection phase. Which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction? The second phase is Objects tracking, which consists of three successive operations, Object segmentation, Object labelling, and Object canter extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like; Simple tracking, object has left the scene, object hasentered the scene, object cross by another object, and object leaves and another one enters the scene. Third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass-by the scene. The final phase is Capturing Object's Picture phase, which captures the image of objects that violate the speed limits. SDCS is implemented and tested in many experiments; it proved to have achieved a satisfactory performance.