Automatic face recognition system poses a various difficult problems using different age faces. Most of the face recognitions have addressed the aging variation problems like age simulation or age estimation. Age invariant is a complex process that affects both the face texture and the shape of the face. In automatic face recognition system these texture and shape changes degrade the performance. The problem is designing an effective matching framework face recognition system using the different aging images. In this paper to develop a model for automatic age invariant face recognition system using facial features. In this model, extract the facial features like eye, mouth and nose in the given image based on the illumination method. Once the eye, nose, and mouth are detected, need to align the face. The face alignment based on the angle between the eye and mouth coordinates. Once the face alignment completed, calculate the eigenvalue and eigenvectors of the covariance matrix. Find the difference between all the features to get a difference vector. The eigenfaces calculates the eigenvalues and eigenvectors. Compare these distance vectors for all faces to get a most match value or minimum mismatch value. Give the face with the most matches or minimum mismatch value as the output. Experimental results show that this method outperforms a using of face aging data sets of FG-NET. A fusion of age invariant face recognition using facial features model further improves the accuracy of the face matching in the presence of aging.
Cite this article:
A. Surendar. Improving Age Invariant Face Recognition System Using Facial Features. Research J. Pharm. and Tech. 2017; 10(6): 1762-1766. doi: 10.5958/0974-360X.2017.00311.0