De-Duplication of Person's Identity Using Multi-Modal Biometrics

The objective of this work is to explore approaches to create unique identities by the de-duplication process using multi-modal biometrics. Various government sectors in the world provide different services and welfare schemes for the beneffit of the people in the society using an identity number. A unique identity (UID) number assigned for every person would obviate the need for a person to produce multiple documentary proofs of his/her identity for availing any government/private services. In the process of creating unique identity of a person, there is a possibility of duplicate identities as the same person might want to get multiple identities in order to get extra beneffits from the Government. These duplicate identities can be eliminated by the de-duplication process using multi-modal biometrics, namely, iris, ngerprint, face and signature. De-duplication is the process of removing instances of multiple enrollments of the same person using the person's biometric data. As the number of people enrolledinto the biometric system runs into billions, the time complexity increases in the de duplication process. In this thesis, three different case studies are presented to address the performance issues of de-duplication process in order to create unique identity of a person

Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

De-noising slap fingerprint images for accurate slap fingerprint segmentation

Fingerprints have unique properties like distinctiveness and persistence. Sometimes, fingerprint images can have some noisy data while capturing them using slap fingerprint scanners. This noise causes improper slap fingerprint segmentation due to which the performance of fingerprint matching decreases. The process of eliminating duplicates is called de-duplication which requires the plain quality fingerprints. While doing the segmentation of slap fingerprints, some of the fingerprint images are improperly segmented because of the noise present in the data. In this paper, an attempt is made to remove the noise present in the slap fingerprint data using binarization of slap fingerprint image, and region labeling of desired regions with 8-adjacency neighborhood for accurate slap fingerprint segmentation. Experimental results demonstrate that the fingerprint segmentation rate is improved from 78% to 99%

Identification of acoustic emission sources in machinery; application to injection/combustion processes in diesel engines

The high temporal resolution of Acoustic Emission offers great promise in the on-line monitoring of complex machines such as diesel engines. The fuel injection process is one of the most important processes in the diesel engine and its timing and fuel delivery control are critical in combustion efficiency. In this work, the phenomena leading to the generation of acoustic emission during injection are investigated by simulation of the injection process in a specially designed rig and through test in running engines on a test-bed. Signal processing approaches are devised to produce diagnostic indicators for the quality of the injection process. The novelty of the research lies in; 1) obtaining a coherent set of data which allows the separation of the part of the signal associated with injection in a given cylinder from other sources adjacent in time and space, and 2) in developing a signal processing approach which allows this separation to be achieved on line using an array of sensors. As such, the research is generic to multi-source multi-sensor analysis in machines. A series of experiments were performed on an experimental injector rig, and two-stroke and four-stroke diesel engines under different operating conditions. The injector rig experiments provided useful information on the characteristic signatures of the injection events, finding which could be implemented to the more complex signal from the running engines. A number of sensor arrays (sets of two and three sensors) were used on two types of four-stroke engine at different running speeds to investigate the source identification of the injection events, the essential strategy being to add complexity to the information in the AE record by using engines of varying degrees of mechanical sophistication. It has been concluded that the AE signals are generated by the mechanical movements of the components in the pump and injector as well as aspects of the fuel flow through the injector and the piping. Also, it is found that the temporal structure of the AE is highly sensitive to sensor position, and that transmission path differences to a sensor array are generally large enough to allow source separation. Applying a purpose-designed thresholding technique, followed by canonical correlation allows the separate identification of parts of the AE signal in the short crank angle widow where sources involved in injection, inlet valve opening and combustion are operating

Multimodal analysis of verbal and nonverbal behaviour on the example of clinical depression

Clinical depression is a common mood disorder that may last for long periods, vary in severity, and could impair an individual’s ability to cope with daily life. Depression affects 350 million people worldwide and is therefore considered a burden not only on a personal and social level, but also on an economic one. Depression is the fourth most significant cause of suffering and disability worldwide and it is predicted to be the leading cause in 2020. Although treatment of depression disorders has proven to be effective in most cases, misdiagnosing depressed patients is a common barrier. Not only because depression manifests itself in different ways, but also because clinical interviews and self-reported history are currently the only ways of diagnosis, which risks a range of subjective biases either from the patient report or the clinical judgment. While automatic affective state recognition has become an active research area in the past decade, methods for mood disorder detection, such as depression, are still in their infancy. Using the advancements of affective sensing techniques, the long-term goal is to develop an objective multimodal system that supports clinicians during the diagnosis and monitoring of clinical depression. This dissertation aims to investigate the most promising characteristics of depression that can be “heard” and “seen” by a computer system for the task of detecting depression objectively. Using audio-video recordings of a clinically validated Australian depression dataset, several experiments are conducted to characterise depression-related patterns from verbal and nonverbal cues. Of particular interest in this dissertation is the exploration of speech style, speech prosody, eye activity, and head pose modalities. Statistical analysis and automatic classification of extracted cues are investigated. In addition, multimodal fusion methods of these modalities are examined to increase the accuracy and confidence level of detecting depression. These investigations result in a proposed system that detects depression in a binary manner (e.g. depressed vs. non-depressed) using temporal depression behavioural cues. The proposed system: (1) uses audio-video recordings to investigate verbal and nonverbal modalities, (2) extracts functional features from verbal and nonverbal modalities over the entire subjects’ segments, (3) pre- and post-normalises the extracted features, (4) selects features using the T-test, (5) classifies depression in a binary manner (i.e. severely depressed vs. healthy controls), and finally (6) fuses the individual modalities. The proposed system was validated for scalability and usability using generalisation experiments. Close studies were made of American and German depression datasets individually, and then also in combination with the Australian one. Applying the proposed system to the three datasets showed remarkably high classification results - up to a 95% average recall for the individual sets and 86% for the three combined. Strong implications are that the proposed system has the ability to generalise to different datasets recorded under quite different conditions such as collection procedure and task, depression diagnosis testing and scale, as well as cultural and language background. High performance was found consistently in speech prosody and eye activity in both individual and combined datasets, with head pose features a little less remarkable. Strong indications are that the extracted features are robust to large variations in recording conditions. Furthermore, once the modalities were combined, the classification results improved substantially. Therefore, the modalities are shown both to correlate and complement each other, working in tandem as an innovative system for diagnoses of depression across large variations of population and procedure