This manuscript presents an improved system research that can detect and recognize the person in 3D space automatically and without the interaction of the people's faces. This system is based not only on a quantum computation and measurements to extract the vector features in the phase of characterization but also on learning algorithm (using SVM) to classify and recognize the person. This research presents an improved technique for automatic 3D face recognition using anthropometric proportions and measurement to detect and extract the area of interest which is unaffected by facial expression. This approach is able to treat incomplete and noisy images and reject the non-facial areas automatically. Moreover, it can deal with the presence of holes in the meshed and textured 3D image. It is also stable against small translation and rotation of the face. All the experimental tests have been done with two 3D face datasets FRAV 3D and GAVAB. Therefore, the test's results of the proposed approach are promising because they showed that it is competitive comparable to similar approaches in terms of accuracy, robustness, and flexibility. It achieves a high recognition performance rate of 95.35% for faces with neutral and non-neutral expressions for the identification and 98.36% for the authentification with GAVAB and 100% with some gallery of FRAV 3D datasets.

In this paper, we present implementation of road sign detection application in an embedded real time system. The target hardware is a Xilinx MicroBlaze soft-processor. The algorithm is described using C language and compiled with the SDK and EDK tools targeting the VirtexML605 FPGA. The input video is a real scene acquired by a digital camera with resolution of 480×640 pixels. Adequate architecture generated of the MicroBlaze with adequate communication with the system memory, allows real time execution of the application. Experimental results on synthetic and real data show that our implementation successfully runs in real time with a low occupation of resources, while maintaining comparable functionality to version existing solutions based on FPGA. The proposed hardware platform is faster compared to existing solutions based DSP.