Automatic human face detection for content-based image annotation

In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

Cascaded face detection using neural network ensembles

We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost

Cascaded Facial Detection Algorithms To Improve Recognition

The desire to be able to use computer programs to recognize certain biometric qualities of people have been desired by several different types of organizations. One of these qualities worked on and has achieved moderate success is facial detection and recognition. Being able to use computers to determine where and who a face is has generated several different algorithms to solve this problem with different benefits and drawbacks. At the backbone of each algorithm is the desire for it to be quick and accurate. By cascading face detection algorithms, accuracy can be improved but runtime will subsequently be increased. Neural networks, once trained, have the ability to quickly categorize objects and assign them identifiers. Combining cascaded face detectors and neural networks, a face in an image can be detected and recognized. In this paper, three different types of facial detection algorithms are combined in various configurations to test the accuracy of face detection at the cost of runtime. By feeding these faces into a convolution neural network, we can begin identifying who the person is