3 research outputs found
Age invariant face recognition system using automated voronoi diagram segmentation
One of the challenges in automatic face recognition is to achieve sequential
face invariant. This is a challenging task because the human face undergoes many
changes as a person grows older. In this study we will be focusing on age invariant
features of a human face. The goal of this study is to investigate the face age invariant
features that can be used for face matching, secondly is to come out with a prototype
of matching scheme that is robust to the changes of facial aging and finally to
evaluate the proposed prototype with the other similar prototype. The proposed
approach is based on automated image segmentation using Voronoi Diagram (VD)
and Delaunay Triangulations (DT). Later from the detected face region, the eyes will
be detected using template matching together with DT. The outcomes, which are list
of five coordinates, will be used to calculate interest distance in human faces. Later
ratios between those distances are formulated. Difference vector will be use in the
proposed method in order to perform face recognition steps. Datasets used for this
research is selected images from FG-NET Aging Database and BioID Face Database,
which is widely being used for image based face aging analysis; consist of 15 sample
images taken from 5 different person. The selection is based on the project scopes
and difference ages. The result shows that 11 images are successfully recognized. It
shows an increase to 73.34% compared to other recent methods
Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
Face recognition is one of the technologies used for assets protection. Face recognition also presents a challenging problem in the field of image and computer vision and has been used for the application such as face tracking and personal identification. It also frequently used in a security system such as a security camera in airport, banks, and offices. Practically, there are problems in improving face recognition performance, particularly for gender identification. It is very difficult to differentiate the person based on face appearance from different poses, lighting, expressions, aging and illumination. Sometimes it is also difficult to identify the shape of human faces because different people have a different structure of faces. This study used image retrieved from Student Information Management Systems (SIMS)from 10 male and 43 female students who're taking MAT530. The image was then generated 12 geometric landmarks using TI nspire software. The main goal of this research is to classify the gender through the images of faces and to resolve for imbalance data using Hellinger Distance Decision Tree (HDDT) classifier. This classifier was proposed as an alternative to decision tree technique which used Hellinger Distance as the splitting criteria. The result from the validation split shows that percentage split at 40% produced the highest value of accuracy rate at 77.2727% and has the most significant value of sensitivity and specificity