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Abstract. In order to the practical face recognition system using in an uncontrolled environment exist many outside interference factors. In this paper, it is proposed that a novel facial standardization method to extract the core region of face samples, furthermore, simultaneously adjust the facial posture and achieve face alignment. It can avoid "mis-alignment disaster" during the feature matching process. In this paper, a video face recognition system of small samples capacity has been established to test this method. The experiments show that the method can make the face samples optimal in the uncontrolled environment and enhance the recognition rate of the face recognition system.

In this paper, a novel system architecture of face detection in possession of modular characteristic is proposed, and the corresponding face detection method is described, to match with the proposed architecture. First of all, the proposed architecture of face detection consists of two modules, namely, the coprocessor module of face detection based on FPGA and target system module, which hopes to implement finial face detection, based on general purpose CPU, and USB bus is used as the communication bridge between the two modules. Secondly, taking the characteristics of FPGA and general purpose CPU into consideration, face detection algorithm can be divided into two layers. The first layer of face detection algorithm based on skin color and eyes' graylevel variation is implemented in the FPGA, and then the corresponding detection results and image are transmitted to the second module by USB bus so as to further detect face using the algorithm combining principle component analysis with support vector machine, which is referred to as the second layer of algorithm. Because the second layer of the algorithms are operations of float-point and loop, it implemented in the general purpose CPU. This architecture enables face detection to be implemented not only in high performance computing platform in possession of USB bus interface, but also in small terminal products and low-end embedded systems, where the performance of processor and the resource of hardware are limited. Actual testing results show that the proposed system architecture can implement real-time face detection for the images with 640 × 480 resolution, and the detection accuracy is about 89 %.

Abstract. Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle's body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA), and set up a 1:10 verification platform of smart car to verify the fuzzy garage parking system with real car. Verification results show that, the system can complete the parking task very well.