Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person's face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.

A recent surge of interest is to recognize Road Signs. Signs are visual languages that represent some special circumstantial information of environment. They provide important information for guiding, warning people to make their movements safer, easier and more convenient. This thesis presents a hybrid neural network solution for Road sign recognition which combines local image sampling and artificial neural network. The method is based on BAM for dimensional reduction and multi-layer perception with backpropagation algorithm has been used for training the network. It has been found from practical observations that the number of iterations required to train the network is enormous. The capability of recognition of a neural network increases with increasing the training accuracy. For this process each sign is converted to a designated M×N feature matrix. These feature matrices of signs are then fed into the neural network as input patterns. The neural network is trained with the set of input patterns of the digits to acquire separate knowledge corresponding to each Road sign. In order to justify the effectiveness of the system, different test patterns of the signs are used to verify the system. Experimental results demonstrate that the system is capable of recognizing Road signs with 98% accuracy.

Optical Character Recognition is of great interest in machine learning and computer vision. Recognition of Bangla Character is a fast forwarded leap to this journey. Neural Network is the field of study in computer learning and its emerging day by day. Time and accuracy is the first concern in learning by machine. Many research works have been accomplished in recognizing Bangla text (both hand written and printed) to achieve high accuracy rate. Neural network is not out of this research work. Back Propagation Neural Network (BPNN) is one of the mostly adopted neural network methodologies in learning and training OCRs. In this research, a comparison is asserted between BPNN and BPNN+BAM (a hybrid network). The hybrid network cuts down the no. of iterations in training the characters awfully in comparison with BPNN. Various number of (2, 4, 6) training images are considered to get the image feature matrix in feeding to the network. Number of iterations and error are observed while the weights are being updated in a optimized level for better recognition of characters with a high accuracy. The iterations in training depends on number of hidden layer used in the network. So, 50% and 70% of hidden layer are used for observation. The iteration decreases more than half of the iteration in BPNN while using BPNN and BAM as hybrid network for dimension reduction of feature matrix.