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Tid: 2020-01-16 14:15
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Implementation and Evaluation of Methods for Contactless Palmprint Recognition

Teodor Breimer och Adam Ly presenterar sitt examensarbete

Implementation and Evaluation of Methods for Contactless Palmprint Recognition


Contactless palmprint recognition is a biometric technology which due to its ease of acquisition, non invasive nature and potentially cheap cost can come to stand competitive to more widely used biometric traits such as finger- or facial recognition. This work uses two different feature extraction methods for palmprint images, the Scale invariant feature transform (SIFT) with an iterative random sample consensus (I-RANSAC) algorithm for refining detected keypoints and the Local Line Directional Pattern (LLDP) which finds the directional line responses in the image by exploiting the Gabor filter in twelve directions. A support vector machine (SVM) classifier is trained to combine the extracted features for the final recognition. A method to extract the region of interest (ROI) is developed for use in a real world setting. Preprocessing techniques are investigated for enhancing the SIFT I-RANSAC and the LLDP matching is refined by a local search algorithm. The proposed method as well as state of the art fingerprint algorithm are evaluated on several different databases: the IITD Palmprint database, the CASIA Palmprint Image database and the Google 11k Hands. In addition to this a database was also collected to reflect the setting and potential problems for a real world contactless recognition system, the Precise Biometrcis Palmprint database. The method shows promising results and the problems and future challenges becomes more clear upon evaluation. To improve the recognition work on the ROI extraction should be the primary focus.