Pattern recognition using statistical and neural techniques

Pattern Recognition has become an attractive research oriented field of the computer vision and machine learning for the last few decades. Neural pattern recognition techniques are also being exercised for pattern recognition, showing promising results. In this paper, a comparison is made between statistical and neural pattern recognition techniques and tried to realize how neural techniques reveal far better results than statistical techniques. In this comparison, Discriminant Analysis (DA) and Principal Component Analysis (PCA) are used for pattern recognition, which are a statistical technique. Discriminant Analysis engrosses the problem of huge data dimensions and small sample size. To evade these problems, pattern recognition task is also implemented using Generalized Regression Neural Network (GRNN) and Back-propagation Neural Network (BPNN) techniques. The task of pattern recognition is conceded on a data base of face images of 400 people. Neural networks proved results for better than statistical methods.