Biometric systems aim to reliably identify and authenticate an individual using physiological or behavioral characteristics. Traditional systems such as the use of access cards, passwords have shown limitations such as forgotten passwords, stolen cards, etc. As an alternative, biometric systems present themselves as efficient systems with a high reliability due to the physiological characteristics of each individual. This paper focuses on a deep learning method for fingerprint recognition. The described architecture uses a pre-processing phase in which grayscale images are represented on the RGB bands and then merged to obtain color images. On the obtained color images will be extracted the characteristics of the fingerprints textures.The fingerprint images after preprocessing are used in a deep convolution network system for decision making. The method is robust with an accuracy of over 99.43% and 99.53% with the respective variants densenet-201 and ResNet-50.
Security systems in companies, airports, enterprises, etc. face numerous challenges. Among the major ones there is objects or face recognition. The problem with the robustness of recognition systems that usually affects color images nowadays can be addressed by multispectral image acquisition in the near infrared range with cameras equipped with new high performance sensors able to take images in dark or uncontrolled environments with much more accuracy. Multispectral CMOS (Complementary Metal Oxide Semi-conductor) sensors in a single shot record several wavelengths that are isolated and allow very specific analyses. They are equipped with new acquisition methods and provide observations that are more accurate. The current generation of these imaging sensors involve scientific and technical interest because they provide much more information than those that operate in visible range; precise nature and spatiotemporal evolution of the areas need to be analyzed. In this study, multispectral images acquired by camera equipped with a hybrid sensor operating in near infrared has been used. This camera is built in the ImViA laboratory of the University of Bourgogne as part of the European project EXIST (EXtended Image Sensing Technologies). The process involved in image acquisition, image mosaicing and image demosaicing by using mosaic filters. After acquisition process the interest points be extract in these bands of images in order to know how information is shared out all over the bands. The results were satisfactory because information is spread all over the images bands and the algorithms used also have detected many interest points. Based on the results, a large database can be set up for a face recognition system building.
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