How to make a survay to do a real time project...with example

[1] Suggests the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. The recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by L^1-minimization they propose a general classification algorithm for (image-based) object recognition.

[2] Suggests a novel technique for face recognition process. Using the statistical local feature analysis (LFA) technique, a set of feature points is extracted from each face image, at locations with highest deviations from the statistical expected face. Each feature point is described by a set of Gabor wavelet responses at different frequencies and orientations. A triangle-inequality-based pruning algorithm is developed for fast matching, which automatically chooses a set of key features from the database of model features and uses the pre-computed distances of the keys to the database, along with the triangle inequality, in order to speedily compute lower bounds on the distances from a query feature to the database, and eliminate the unnecessary direct comparisons.
[3] Suggests a Multilayer Perceptron Neural Network (NN) for access control based on face image recognition. A new thresholding approach for rejection of unauthorized persons is proposed. Ensembles of NN with different architectures are proposed here. Advantages of the ensembles are shown, and the best architecture parameters are given. The usage of negative examples was explored. We have shown that by using negative examples we can improve performance for access control task. The explored NN architectures may be used in real-time applications.

[4] Suggests a prototype system does the facial match by utilizing multi-algorithmic multi-biometric technique, combining gray level statistical correlation method with Principal Component Analysis (PCA) or Discrete Cosine Transform (DCT) techniques in order to boost the system performance. Based on a comparison of the extracted signature with the set of references, the set of top five hits are selected. After interpolating the face to the exact scale, matching scores are computed based on gray level correlation of a number of features on the face.

[5] Suggests the Yale face database process The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions.

[6] Suggests a PCA algorithm used in face recognition system and its implementation on different architectures in order to choose the best solution for designing a real time face recognition system. Benchmarks and comparisons will be given for PC, DSP and FPGA and results will show that FPGA soft core is too slow for this computation.