Analysis of Soft Computing Techniques for Face Detection

Abstract

Soft computing techniques are a good solution for the face detection. Neural network is one of the soft computing techniques, which are generally used for learning and training process. Face detection is one of the challenging problems in the image processing. The basic aim of face detection is determine if there is any face in an image. And then locate position of a face in image. Human face detected in an image can represent the presence of a human in a place. Evidently, face detection is the first step towards creating an automated system, which may involve other face processing. A novel face detection system is presented in this research work. The approach relies on neural networks, which can be used to detect faces by using FFT. The neural network is created and trained with training set of faces and non-faces. The network used is a two layer feed-forward neural network. There are two modifications for the classical use of neural networks in face detection. First, the neural network tests only the face candidate regions for faces, thus the search space is reduced. Second, the window size used by the neural network in scanning the input image is adaptive and depends on the size of the face candidate region. The objective of this work was to implement a classifier based on MLP (Multi-layer Perception) neural networks for face detection. The MLP was used to classify face and non-face patterns. Soft computing techniques, which emphasize gains in understanding system behavior in exchange for unnecessary precision, have proved to be important practical tools for many existing problems. NNs are approximations of any multivariate function because they can be used for modeling highly nonlinear, unknown, or partially known complex systems, plants, or processes. We made four combinations FFT_TRAINSCG, DCT_TRAINSCG, FFT_TRAINCGB and DCT_TRAINCGB for face detection and compare the results.