Signal processing and machine learning techniques for human verification based on finger textures

In recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-specific features, such as wrinkles and apparent lines distributed along the inner surface of all fingers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is first proposed to isolate finger images from a hand area. It is able to detect the fingers as objects from a hand image. An efficient adaptive finger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to efficiently analyse the FT patterns. As a result, a powerful human verification scheme based on finger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the finger objects. The verification performances are examined in the case of missing FT areas. Consequently, to overcome finger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies confirm the efficiency of the proposed methods for human verification. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the verification performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN. Statement of Originality The contributions of this thesis have been supported by different journal and conference papers, which have been generated during the journey of my study. They can be stated as follows: [1] R. R. O. Al-Nima, M. A. M. Abdullah, M. T. S. Al-Kaltakchi, S. S. Dlay, W. L. Woo, and J. A. Chambers, “Finger Texture Biometric Verification Exploiting a Multi-scale Sobel Angles Local Binary Pattern and Score-based Fusion”, Elsevier, Digital Signal Processing, vol. 70, 2017. https://doi.org/10.1016/j.dsp. 2017.08. 002 The originality of this paper can be found in Chapter 5 and Chapter 6. That is, the Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction approach is explained and evaluated in Chapter 5. Furthermore, the innovative fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is described and examined in Chapter 6. Moreover, the Receiver Operating Characteristic (ROC) curves for the FCFNN are depicted in this paper and how these curves are produced is clarified in Appendix A. [2] R. R. O. Al-Nima, S. S. Dlay, W. L. Woo and J. A. Chambers, “Efficient Finger Segmentation Robust to Hand Alignment in Imaging with Application to Human Verification,” 5th IEEE International Workshop on Biometrics and Forensics (IWBF), 2017. The contribution of this paper is a novel segmentation approach, which is illustrated in Chapter 4 under the name Adaptive and Robust Finger Segmentation (ARFS). In addition, an efficient finger fusion scheme is explained in the same paper and included in Chapter 6 under the term Feature Level Fusion with Probabilistic Neural Network (FLFPNN). [3] R. R. O. Al-Nima, S. S. Dlay, S. A. M. Al-Sumaidaee, W. L. Woo and J. A. Chambers, “Robust feature extraction and salvage schemes for finger texture based biometrics,” in IET Biometrics, vol. 6, no. 2, 2017. doi: 10.1049/iet-bmt.2016. 0090. In this paper four contributions are presented. Firstly, the Robust Finger Segmentation (RFS) approach, which can be found in Chapter 4. Secondly, the Enhanced Local Line Binary Pattern (ELLBP) feature extraction method, which can be seen in Chapter 5. Thirdly, evaluating the missing FT elements, which is included in Chapter 6. Fourthly, applying the salvage approach, which is involved in Chapter 6 too. [4] R. R. O. Al-Nima, S. S. Dlay, W. L. Woo and J. A. Chambers, “A novel biometric approach to generate ROC curve from the Probabilistic Neural Network,” 24th IEEE Signal Processing and Communication Application Conference (SIU), Zonguldak, 2016. doi: 10.1109/SIU.2016. 7495697. In this paper a new method to generate the ROC graph from a Probabilistic Neural Network (PNN) is introduced. The details of this method can be found in Appendix A. [5] R. R. O. Al-Nima, S. S. Dlay, W. L. Woo and J. A. Chambers, “Human authentication with finger textures based on image feature enhancement,” 2nd IET International Conference on Intelligent Signal Processing (ISP), London, 2015. doi: 10.1049/cp.2015. 1784. This was my first contribution paper related to verifying people by using the Finger Texture (FT) characteristic. It has been utilized in Chapter 4 for comparisons. Moreover, additional papers have been established during my study. They can be considered as extending supporting work and they indirectly relate to this thesis. These are as follows: [6] S. A. M. Al-Sumaidaee, M. A. M. Abdullah, R. R. O. Al-Nima, S. S. Dlay and J. A. Chambers, “Multi-gradient Features and Elongated Quinary Pattern Encoding for Image-based Facial Expression Recognition,” Elsevier, Pattern Recognition, vol. 71, 2017. [7] M. A. M. Abdullah, R. R. Al-Nima, S. S. Dlay, W. L. Woo and J. A. Chambers, “Cross-spectral Iris Matching for Surveillance Applications,” Springer, Surveillance in Action Technologies for Civilian, Military and Cyber Surveillance, Chapter 5, 2017. https://doi.org/10.1007/978-3-319-68533-5 5. [8] R. R. O. Al-Nima, M. T. S. Al-Kaltakchi, S. S. Dlay, W. L. Woo, and J. A. Chambers, “Personal Verification Based on Multi-Spectral Finger Texture Lighting Images”, In preparation for submission to IET Signal Processing, 2017. [9] R. R. O. Al-Nima, S. S. Dlay, W. L. Woo, “A New Approach to Predicting Physical Biometrics from Behavioural Biometrics,” World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 8, No. 11, 2014.