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Abstract
Biometric identification is the automated technique of measuring the biological data. The term biometrics is commonly used today
to recognize a person by analyzing his/her physical characteristics and comparing these characteristics to a database such as
fingerprint recognition, retinal scanning, face recognition etc. Face recognition is one of the biometric techniques that are used for
identifying and verifying the identity of a person. This paper presents the design and implementation of a face recognition system
for Android mobile phone platform. The design methodology includes two main steps. The first step is the extraction of the image’s
features and the second one is the recognition according to the classification of patterns. This system includes face detection using
face detector available in the Android called object detection (Google's API mobile vision), features extraction (nose detection,
mouth detection, eyes detection and cheek detection) using the same algorithm object detection API. From the feature extraction,
we have 30 geometrical measurements of each person. The recognition step is done by calculating the Pearson Correlation
Coefficients between the test image’s geometrical measurements and measurements stored in the training database. The system
will accept a facial image of the person as authenticated and allow to access the mobile applications if the percentage of correlation
is greater than a chosen threshold otherwise it rejects the image. The recognition rate of the proposed approach has achieved an
accuracy more than 95% of other approaches
