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Abstrak :
Pencurian identitas menjadi ancaman kejahatan di dunia maya pada masa kini, khususnya transaksi online. Untuk mengatasi masalah tersebut, voice biometrics dikembangkan untuk keamanan identitas. Penelitian ini mengusulkan skema voice biometrics pada algoritma deep learning Convolutional Neural Network (CNN) Residual dan CNN Depthwise Separable Convolution (DSC) dengan fitur ekstraksi \hybrid Discrete Wavelet Transform (DWT) dan Mel Frequency Cepstral Coefficients (MFCC) serta mengembangkan pembuatan data suara untuk pengguna ber-Bahasa Indonesia dalam waktu 25 menit. Skema tersebut ditargetkan untuk meningkatkan kinerja akurasi. Penelitian ini mengembangkan 2 model simulasi yang terpisah, yaitu model CNN Residual dan CNN DSC. Untuk setiap pengujian model, hasilnya dibandingkan dengan CNN Standard. Hasil pengujian pertama menunjukkan kinerja terbaik, model CNN Residual ini mampu meningkatkan kinerja validasi akurasi training voice biometrics 98.6345%, presisi 99,91% dan akurasi 99,47% pada speaker recognition (siapa yang bicara?), serta akurasi speech recognition (apa yang diucapkan?) 100%. Hasil pengujian kedua menunjukkan kinerja terbaik, model CNN DSC ini mampu mengurangi kinerja training parameter dan mampu mempercepat kinerja waktu proses training voice biometrics menjadi 5,12 detik. Sehingga hasil kinerja tersebut dapat mengurangi beban komputasi dan lebih baik dalam kinerja akurasinya. Dapat disimpulkan bahwa CNN Residual dan CNN DSC telah mengungguli CNN Standard. Sehingga pengembangan skema voice biometrics dapat diaplikasikan untuk identifikasi dan verifikasi/autentikasi suara user secara akurat, efisien dan cepat untuk aplikasi keamanan identitas dalam transaksi perbankan. ......Theft of identity is a threat to cybercrime today, especially online transactions. To overcome this problem, voice biometrics was developed for identity security. This research proposes a voice biometrics scheme on deep learning algorithms the CNN Residual and CNN Depthwise Separable Convolution (DSC) with Hybrid of Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC) Feature Extraction and develops voice data establishment for Indonesian users within a short period of time 25 minutes. The scheme is targeted to improve accuracy performance. This research developed 2 separate models, i.e. CNN Residual and CNN DSC model. For each model testing, the results are compared with the CNN Standard. The results of the first testing show the best performance, the CNN Residual model is able to improve the performance of training accuracy validation on voice biometrics of 98.6345%, precision of 99.91% and accuracy of 99.47% on speaker recognition (who is speaking?), and accuracy on speech recognition (What is uttered?) of 100%. The results of the second testing show the best performance, the CNN DSC model is able to reduce the performance of training parameters and is able to accelerate the performance of the voice biometrics training process time to 5.12 seconds. So that the performance results can reduce the computational load and and better in its accuracy performance. It can be concluded that CNN Residual and CNN DSC have outperformed CNN Standard. So that the development of voice biometrics schemes can be applied for identification and verification/authentication of the user's voice accurately, efficiently and quickly for identity security applications in banking transactions.

Abstrak :
This book presents the latest developments in biometrics technologies and reports on new approaches, methods, findings, and technologies developed or being developed by the research community and the industry. The book focuses on introducing fundamental principles and concepts of key enabling technologies for biometric systems applied for both physical and cyber security. The authors disseminate recent research and developing efforts in this area, investigate related trends and challenges, and present case studies and examples such as fingerprint, face, iris, retina, keystroke dynamics, and voice applications . The authors also investigate the advances and future outcomes in research and development in biometric security systems. The book is applicable to students, instructors, researchers, industry practitioners, and related government agencies staff. Each chapter is accompanied by a set of PowerPoint slides for use by instructors.

Abstrak :
This timely text/​reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

Abstrak :
This book also introduces biometric applications in our environment, which further illustrates the close relationship between Biometrics and kansei engineering. Examples and case studies are provided throughout this book.

Abstrak :
This special issue contains five selected papers that were presented at the Workshop on Pattern Recognition for IT Security, held in Darmstadt, Germany, in September 2010, in conjunction with the 32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010. It demonstrates the broad range of security-related topics that utilize graphical data. The contributions explore the security and reliability of biometric data, the power of machine learning methods to differentiate forged images from originals, the effectiveness of modern watermark embedding schemes and the use of information fusion in steganalysis.

Abstrak :
This book constitutes the refereed proceedings of the 5th International Conference on Image and Signal Processing, ICISP 2012, held in Agadir, Morocco, in June 2012. The 75 revised full papers presented were carefully reviewed and selected from 158 submissions. The contributions are grouped into the following topical sections, multi/hyperspectral imaging, image itering and coding, signal processing, biometric, watermarking and texture, segmentation and retieval, image processing, and pattern recognition.

Abstrak :
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shape, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Abstrak :
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.