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

A study on feature extraction for face recognition using Self Organizing Maps

This paper deals with the study related to the face recognition algorithms and process of developing a Self-Organizing Map (SOM) in order to carry out the process of face recognition in case of humans. Initially the paper deals with the various general steps involved in the structure and statistics based face recognition algorithms. However in the later part the key step used in the unsupervised algorithm as well as the combination of SOM and Hierarchical Self Organizing Map (HSOM) along with the aid of Gabor filters were discussed in order to carry out an efficient process of facial recognition. The feature selection criteria are also discussed in detail in order to achieve a high end result.Keywords:SOM, HSOM, Gabor filters, unsupervised learning, feature extractio

Empirical mode decomposition-based facial pose estimation inside video sequences

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions