A single reference image is used for detecting the Face area in gray scale images. The algorithm uses Gabor transform as a powerful tool for feature extraction. Zero-crossing of Gabor transform is used for constructing the binary feature vectors and Hamming distance is used for measuring the similarity between feature vectors. Our developed method uses a pyramidal structure for finding the Face images with different sizes. The algorithm has considerable robustness to the cluttered backgrounds and non-uniform illumination. More than 11,000 Face images have been tested. The results show a very high Detection rate for non occluded Face images with low facial expression. The algorithm can also be used for correcting the head rotation angle in input images. The algorithm can also be used for correcting the head rotation angle in input images. The proposed method is suitable for the cases that contain one Face image.

Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the Face. The aim of this thesis is to solve the Face-Detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal Face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’ t be solved by any of the existing methods but can be solved by our proposed method.

General boosting algorithms for Face Detection use rectangular features. To obtain a better performance, it needs more training samples and may generate an unpredictable number of features. Besides using pixel values, which are easily affected by illumination, to calculate the rectangular features, it usually needs to preprocess the data before calculating the values of the features. Such an approach may increase computation time. To overcome the drawbacks, we propose a new solution based on the Adaboost algorithm and the Back Propagation Network (BPN) of a Neural Network (NN), combining local and global features with cascade architecture to detect human Faces. We use the Modified Census Transform (MCT) feature, which belongs to texture features and is less sensitive to illumination, for local feature calculation. In this approach, it is not necessary to preprocess each sub-window of the image. For classification, we use the structure of the hierarchical feature to control the number of features. With only MCT, it is easy to misjudge Faces and, therefore, in this work, we include the brightness information of global features to eliminate the False Positive (FP) regions. As a result, the proposed approach can have a Detection Rate (DR) of 99%, an FPs of only 11, and Detection speed of 27.92 Frames Per Second (FPS).