AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching

يعد التحقق من بصمة الأصبع أحد الطرق الحديثة في مجال أمن المعلومات والذي يهدف إلى إيجاد أنماط مميزة للتعرف على هوية الفرد. يتم ذلك عبر عملية مقارنة بين أزواج من نماذج معدة مسبقا للبصمة وإيجاد نسبة التشابه بينهم. غالبية الدراسات السابقة كانت تعتمد على طريقة تدعى (فازي فالت) بالإضافة إلى طرق فلترة الصور. لكن هذه الطرق لا تزال تعاني من ضعف تمييز النقاط المهمة في البصمات، ظهور التقنيات الحديثة من التعلم العميق مثل الشبكات العصبية اللفائفية قد ساهم بشكل كبير في تحليل الصورة والتعرف على الكيانات داخل الصور وقدأظهرت دقة أعلى من الطرق التقليدية. هذه الدراسة استغلت إحدى هذه الشبكات المدربة مسبقا على صور بصمات وتعرف باسم (اليكس نت) بحيث تم استخراج أهم الخصائص الكامنة بالصور وتم توليد مفتاح خاص بكل صورة ومن ثم تم تخزين كل تلك المعلومات في قاعدة بيانات مرجعية. باستخدام أدوات قياس التشابه مثل جتا الزاوية  وهامنج استطاعت هذه الدراسة من تبيان التشابه خلال مقارنة صور اختبارية بالنسبة لقاعدة البيانات المرجعية. تم استجلاب الصور من قاعدة بيانات عامة وقد أظهرت نتائج دقة القبول دقة الرفض على نسبة 2.09% و 2.81% على التوالي. بمقارنة هذه النتائج مع نتائج الدراسات السابقة خصوصا تلك التي استخدمت أدوات تقليدية مثل (فازي فالت) تفوق الطريقة المطروحة بهذه الدراسة. وبذلك تم استنتاج أهمية استخدام الشبكات العصبية اللفائفية مع أدوات قياس التشابه في التعرف على بصمة اليد.In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching

A SIFT-Based Fingerprint Verification System Using Cellular Neural Networks

Recently, with the increasing demand of high security, person identification has become more and more important in our everyday life. The purpose of establishing the identity is to ensure that only a legitimate user, and not anyone else, accesses the rendered services. The traditional identification methods are based on “something that you possess ” and “somethin

지문 영상 잡음 제거 및 복원을 위한 심층 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2021. 2. 강명주.Biometric authentication using fingerprints requires a method for image denoising and inpainting to extract fingerprints from degraded fingerprint images. A few deep learning models for fingerprint image denoising and inpainting were proposed in ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018. In this thesis, a new deep learning model for fingerprint image denoising is proposed. The proposed model is adapted from FusionNet, which is a convolutional neural network based deep learning model for image segmentation. The performance of the proposed model was demonstrated using the dataset from the ECCV 2018 ChaLearn Competition. It was shown that the proposed model obtains better results compared with the models that achieved high performances in the competition.지문을 사용한 생체 인식 인증은 품질이 저하된 지문 영상에서 지문을 추출하기 위한 영상 잡음 제거 및 복원 방법을 필요로 한다. 지문 영상 잡음 제거 및 복원을 위한 몇 가지 딥러닝 모델이 ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018에서 제안되었다. 본 논문에서는 지문 영상 잡음 제거를 위한 새로운 딥러닝 모델을 제안한다. 제안된 모델은 영상 분할을 위한 합성곱 신경망 기반 딥러닝 모델인 FusionNet을 수정하여 작성하였다. 제안된 모델의 성능은 ChaLearn Competition의 데이터셋을 사용하여 검증되었다. 이를 통해 제안된 모델이 대회에서 높은 성능을 획득한 다른 모델들에 비하여 더 나은 결과를 얻음을 확인하였다.Abstract i Contents ii 1 Introduction 1 2 Related Work 3 2.1 Residual Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Convolutional Neural Networks for Semantic Segmentation . . . . . . 4 2.2.1 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.2 FusionNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Recent Trends in Fingerprint Image Denoising . . . . . . . . . . . . . 6 3 Proposed Model 7 3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Architecture Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Residual Block . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.4 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Experiments 13 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.1 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.2 Comparison with Other Models . . . . . . . . . . . . . . . . 17 5 Conclusion 21 Abstract (In Korean)Maste

Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods

Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

Enhanced convnet based Latent Finger Print Recognition

Latent finger print recognition plays an important role in forensic, criminal cases etc. The latent images will not be recognised easily since they are impartial images, which find difficult to match with the registered database. Due to noisy images, it is very difficult for recognition. Autoencoder plays an important role in pre-processing the latent image. ConvNetbased method is an efficient approach used for latent image recognition. For each minutiae extraction, ConvNet descriptor is performed. Both minutiae and texture matcher is considered for comparison. This technique is compared with existing methods which shows, that the proposed method provides a higher accuracy than the existing methods like CNN, skeleton approach nonlinear mapping and product quantization. The proposed method provides an accuracy of 76.4%, 80.4% and 86.4% for rank1,5 and 10 respectively