Face recognition using local mapped pattern and genetic algorithms
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Facial recognition is one of the most used biometric technologies in automated systems which ensure a person’s identity for authorizes access and monitoring. The acceptance of face use has several advantages over other biometric technologies since it is natural, it does not require sophisticated equipment, data acquisition is based on non-invasive approaches, and it can be done remotely, cooperatively or not. Although many facial recognition studies have been done, problems with light variation, facial occlusion, position, expression, and aging are still challenges, because they influence the performance of facial recognition systems and motivate the development of more reliable recognition systems that deal with these problems. In this paper, we describe the Multi-Scale Local Mapped Pattern (MSLMP) applied for facial recognition. Techniques based on genetic algorithms and image processing were applied to increase the performance of the method. The obtained results reach up to 100% of accuracy for some face Database. A very difficult database to deal is the MUCT database which was created in 2010 with the aim of providing images with a high variation of lighting, age, positions, and ethnicities in the facial biometry literature, which makes it a highly difficult database in relation to automated recognition. A new processing technique was developed based on the average gray levels of the images of the database for deal with difficult databases like MUCT. The results obtained with our techniques for MUCT database are superior to results obtained for recognition techniques applied to this database available in the literature.