Examinando por Materia "Palmprint"
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- PublicaciónSólo datosDevelopment of a Method for Identifying People by Processing Digital Images from Handprint(Lecture Notes in Computer Science, 2021-06-16) Tuesta Monteza, Victor A.; Cespedes-Ordoñez, Barny N.; Mejia-Cabrera, Heber I.; Forero, Manuel G.Fingerprint recognition methods present problems due to the fact that some prints are blurred or have changes due to the activities carried out with the hands by some people. In addition, these identification methods can be violated by using false fingerprints or other devices. Therefore, it is necessary to develop more reliable methods. For this purpose, a handprint-based identification method is presented in this paper. A database was built with the right handprints of 100 construction workers. The method comprises an image pre-processing and a classification stage based on deep learning. Six neural networks were compared VGG16, VG19, ResNet50, MobileNetV2, Xception and DenseNet121. The best results were obtained with the RestNet50 network, achieving 99% accuracy, followed by Xception with 97%. Showing the reliability of the proposed technique.
- PublicaciónSólo datosNew method for subject identification based on palm print(Proceedings of SPIE - The International Society for Optical Engineering, 2020-08-24) Mejía-Cabrera, Heber I.; Antón Chiclayo, Rafael; Flores, J. Nicolás; Tuesta-Monteza, Victor; G. Forero, ManuelNowadays, new security and protection systems for citizens are being developed, since criminals have found techniques to violate those already known, such as those based on fingerprints, facial recognition, iris and voice. Thus, using biometric data, new systems are being developed that are more secure, infallible and fast to identify each person, making it impossible to impersonate them, as has happened with other methods. Recently new identification methods have been proposed based on hand geometry and palmprint based on texture techniques for the identification of hand characteristics such as ridges, edges, points, and textures. Following this trend, this paper presents a method based on the detection of the palm print, acquired by contact, through the use of a scanner. For this purpose, the image is segmented to detect the silhouette of the hand and delimit the working area, achieving greater speed in identification. The images are then used as input to a convolutional neural network VGG 16 for learning and identification of subjects, achieving 100% accuracy.