Robust Image Recognition Based on a New Supervised Kernel Subspace Learning Method

Fecha de lectura de Tesis Doctoral: 13 de septiembre 2019Image recognition is a term for computer technologies that can recognize certain people, objects or other targeted subjects through the use of algorithms and machine learning concepts. Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only is a nonlinear and complex variation of face images effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. In this research, we particularly focus on face recognition however, two other types of databases rather than face databases are also applied to well investigate the implementation of our algorithm. Experimental results reveal that our method consistently outperforms its competitors across a wide range of dimensionality on all the datasets. SKLDNE method has reached 100 percent of recognition rate for Tn=17 on the Sheffield, 9 on the Yale, 8 on the ORL, 7 on the Finger vein and 11on the Finger Knuckle respectively, while the results are much lower for other methods. This demonstrates the robustness and effectiveness of the proposed method

Log-Gabor Filter Based Finger Vein Biometric System Using Modified Repeated Line Tracking Algorithm

Prestasi sistem pengecaman vena jari bergantung pada kualiti imej yang ditangkap. Walaupun topeng penyahtajaman lelurus klasik mampu mempertingkatkan bahagian gelap dan bayang-bayang imej urat jari, tetapi imej yang dipertingkatkan akan mengalami dua kekurangan. Pertama, kesan halo yang muncul di sekitar kawasan imej yang lebih tajam. Kedua, hingar yang wujud dalam imej juga akan dipertingkatkan. Kajian ini mengubah topeng penyahtajaman lelurus klasik dengan menggunakan penapis Log-Gabor. Topeng Penyahtajaman Diperbaiki (MUM) meningkatkan kontras dan ketajaman imej tanpa kelemahan yang disebutkan di atas. Kajian ini memperkenalkan peringkat pra-pemprosesan dalam sistem pengesahan vein jari yang mana, mulanya, kaedah penyamaan Histogram Pengesuaian Had (CLAHE) akan digunakan pada imej masukan dan kemudiannya teknik MUM digunakan untuk meningkatkan ketajaman dan kontras imej urat jari. Hasil daripada ciri yang diekstrak menunjukkan peningkatan yang cemerlang dalam mengenalpasti perincian vena dengan menggunakan kaedah prapemprosesan yang dicadangkan ini. Penjejakan Garis Ulangan Terubahsuai (MRLT) digunakan sebagai kaedah pengekstrakan ciri Manakala Mesin Vektor Sokongan (SVM) digunakan sebagai pengelas. Kadar Kesalahan Seimbang (EER) digunakan sebagai pengiraan prestasi dalam kajian ini. EER yang diperolehi untuk sistem pengesahan dengan meggunakan tiga data latihan ialah 16.66% untuk imej asal, 14.22% untuk imej CLAHE yang dipertingkatkan dan 6.28% untuk imej bagi kaedah yang dicadangkan (CLAHE kemudian MUM). _______________________________________________________________________________________________________ The performance of finger vein recognition system relies on the quality of captured image. Although the classical linear Un-sharp mask can enhance the dark and shadowy parts of finger vein image, but the enhanced image suffers two drawbacks. First, the halo effects that appears around sharper areas of image. Second, the noises which exist in image are over enhanced. This study modifies the classical linear Un-sharp mask with use of Log-Gabor filter. This Modified Un-sharp Mask (MUM) enhances the contrast and sharpness of image without aforementioned drawbacks. This study, introduced a pre-processing stage in the finger vein verification system which first, applies Contrast Limit Adaptive Histogram Equalization (CLAHE) method on input image then use MUM technique in order to enhance the sharpness and contrast of finger vein image. The results of extracted feature show the excellent improvement in detection of vein details by using the proposed pre-processing method. The Modified Repeated Line Tracking (MRLT) is used as feature extraction method and Support Vector Machine (SVM) is used as classifier. The Equal Error Rate (EER) is used as performance evaluation in this study. The EERs for the verification system at three training data is observed to be 16.66% for original image, 14.22% for CLAHE enhanced image and 6.28% for proposed method (CLAHE then MUM)

Biometrics & [and] Security:Combining Fingerprints, Smart Cards and Cryptography

Since the beginning of this brand new century, and especially since the 2001 Sept 11 events in the U.S, several biometric technologies are considered mature enough to be a new tool for security. Generally associated to a personal device for privacy protection, biometric references are stored in secured electronic devices such as smart cards, and systems are using cryptographic tools to communicate with the smart card and securely exchange biometric data. After a general introduction about biometrics, smart cards and cryptography, a second part will introduce our work with fake finger attacks on fingerprint sensors and tests done with different materials. The third part will present our approach for a lightweight fingerprint recognition algorithm for smart cards. The fourth part will detail security protocols used in different applications such as Personal Identity Verification cards. We will discuss our implementation such as the one we developed for the NIST to be used in PIV smart cards. Finally, a fifth part will address Cryptography-Biometrics interaction. We will highlight the antagonism between Cryptography – determinism, stable data – and Biometrics – statistical, error-prone –. Then we will present our application of challenge-response protocol to biometric data for easing the fingerprint recognition process