IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION
Journal: ICTACT Journal on Image and Video Processing (IJIVP) (Vol.4, No. 2)Publication Date: 2013-11-01
Authors : Muthukannan K Latha P; Manimaran C;
Page : 690-694
Keywords : Face Detection; Gabor Wavelet; Feed Forward Neural Network Classifier;
Abstract
Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.
Other Latest Articles
- ANALYSIS OF TARSAL TUNNEL SYNDROME USING IMAGE CORRELATION
- A MEDICAL MULTI-MODALITY IMAGE FUSION OF CT/PET WITH PCA, DWT METHODS
- COMPARATIVE ANALYSIS OF DS AND IDS ALGORITHMS IN SUPER-SPATIAL STRUCTURE PREDICTION FOR MEDICAL IMAGE SEQUENCES
- REMOVAL OF IMPULSIVE NOISE USING WEIGHTED FUZZY MEAN FILTER BASED ON CLOUD MODEL
- A WAVELET TRANSFORM BASED WATERMARKING ALGORITHM FOR PROTECTING COPYRIGHTS OF DIGITAL IMAGES
Last modified: 2013-12-05 18:27:00