Face Detection Using Gabor Feature Extraction And Artificial Neural Network Pdf
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- Applying Artificial Neural Networks for Face Recognition
- Face Detection Using Gabor Feature Extraction and Artificial Neural Network
- Novel Face Detection Using Gabor Filter Bank with Variable Threshold
Face detection is the method of locating human faces in a given image under all lighting conditions, scales and orientations. Face is a unique feature of every person and the same is applicable to pupil, iris and fingerprints which are also unique as well. Automatic face detection and recognition has been drawing the main attention in the recent years. We have proposed here an accurate face detection system that can detect faces under different contrast with hurdles like faces with spectacles, heavy beard and even closed eyes. We use Gabor filter bank with varying threshold for feature extraction and face detection.
Applying Artificial Neural Networks for Face Recognition
This paper introduces some novel models for all steps of a face recognition system. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face.
The obtained results exhibited better face recognition rates in a shorter execution time compared to the GOM technique. Because of the increase and complexity of criminal behavior in modern societies, robust and efficient authentication systems became vital. Compared to traditional token Id Card, badge, … and knowledge-based systems PIN, Password, … , which can be either forgotten or stolen, biometric authentication systems offer a better solution for a wide range of applications like access control, criminal identification, etc. Different modalities are used in biometric systems such as iris, fingerprint, voice, and face recognition. The latter is widely used for many factors.
Face Detection Using Gabor Feature Extraction and Artificial Neural Network
Facial Recognition System has been widely used in various applications. Nevertheless, their efficiency rate fell dramatically when they were applied under unrestrained environments like the position of face, expression or illumination change. Because of these factors, it is essential to measure and calculate the performance rate of the dissimilar feature extraction techniques robust to such transformations in order to further integrate to a Facial Recognition System. This paper studies and evaluates the Histogram of Oriented Gradients method as a feature extraction method in order to deal with the abovementioned transformations. The study consists of four main phases: face detection, preprocessing, features extraction, and classification. Preprocessing is used to enhance the images by using the techniques of digital image processing.
Novel Face Detection Using Gabor Filter Bank with Variable Threshold
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Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the s, but the problem is still largely unsolved.
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