Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Plant Seed Identification

Plant seed identification is routinely performed for seed certification in seed trade, phytosanitary certification for the import and export of agricultural commodities, and regulatory monitoring, surveillance, and enforcement. Current identification is performed manually by seed analysts with limited aiding tools. Extensive expertise and time is required, especially for small, morphologically similar seeds. Computers are, however, especially good at recognizing subtle differences that humans find difficult to perceive. In this thesis, a 2D, image-based computer-assisted approach is proposed. The size of plant seeds is extremely small compared with daily objects. The microscopic images of plant seeds are usually degraded by defocus blur due to the high magnification of the imaging equipment. It is necessary and beneficial to differentiate the in-focus and blurred regions given that only sharp regions carry distinctive information usually for identification. If the object of interest, the plant seed in this case, is in- focus under a single image frame, the amount of defocus blur can be employed as a cue to separate the object and the cluttered background. If the defocus blur is too strong to obscure the object itself, sharp regions of multiple image frames acquired at different focal distance can be merged together to make an all-in-focus image. This thesis describes a novel non-reference sharpness metric which exploits the distribution difference of uniform LBP patterns in blurred and non-blurred image regions. It runs in realtime on a single core cpu and responses much better on low contrast sharp regions than the competitor metrics. Its benefits are shown both in defocus segmentation and focal stacking. With the obtained all-in-focus seed image, a scale-wise pooling method is proposed to construct its feature representation. Since the imaging settings in lab testing are well constrained, the seed objects in the acquired image can be assumed to have measureable scale and controllable scale variance. The proposed method utilizes real pixel scale information and allows for accurate comparison of seeds across scales. By cross-validation on our high quality seed image dataset, better identification rate (95%) was achieved compared with pre- trained convolutional-neural-network-based models (93.6%). It offers an alternative method for image based identification with all-in-focus object images of limited scale variance. The very first digital seed identification tool of its kind was built and deployed for test in the seed laboratory of Canadian food inspection agency (CFIA). The proposed focal stacking algorithm was employed to create all-in-focus images, whereas scale-wise pooling feature representation was used as the image signature. Throughput, workload, and identification rate were evaluated and seed analysts reported significantly lower mental demand (p = 0.00245) when using the provided tool compared with manual identification. Although the identification rate in practical test is only around 50%, I have demonstrated common mistakes that have been made in the imaging process and possible ways to deploy the tool to improve the recognition rate

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Temporal Segmentation of Human Actions in Videos

Understanding human actions in videos is of great interest in various scenarios ranging from surveillance over quality control in production processes to content-based video search. Algorithms for automatic temporal action segmentation need to overcome severe difficulties in order to be reliable and provide sufficiently good quality. Not only can human actions occur in different scenes and surroundings, the definition on an action itself is also inherently fuzzy, leading to a significant amount of inter-class variations. Moreover, besides finding the correct action label for a pre-defined temporal segment in a video, localizing an action in the first place is anything but trivial. Different actions not only vary in their appearance and duration but also can have long-range temporal dependencies that span over the complete video. Further, getting reliable annotations of large amounts of video data is time consuming and expensive. The goal of this thesis is to advance current approaches to temporal action segmentation. We therefore propose a generic framework that models the three components of the task explicitly, ie long-range temporal dependencies are handled by a context model, variations in segment durations are represented by a length model, and short-term appearance and motion of actions are addressed with a visual model. While the inspiration for the context model mainly comes from word sequence models in natural language processing, the visual model builds upon recent advances in the classification of pre-segmented action clips. Considering that long-range temporal context is crucial, we avoid local segmentation decisions and find the globally optimal temporal segmentation of a video under the explicit models. Throughout the thesis, we provide explicit formulations and training strategies for the proposed generic action segmentation framework under different supervision conditions. First, we address the task of fully supervised temporal action segmentation, where frame-level annotations are available during training. We show that our approach can outperform early sliding window baselines and recent deep architectures and that explicit length and context modeling leads to substantial improvements. Considering that full frame-level annotation is expensive to obtain, we then formulate a weakly supervised training algorithm that uses ordered sequences of actions occurring in the video as only supervision. While a first approach reduces the weakly supervised setup to a fully supervised setup by generating a pseudo ground-truth during training, we propose a second approach that avoids this intermediate step and allows to directly optimize a loss based on the weak supervision. Closing the gap between the fully and the weakly supervised setup, we moreover evaluate semi-supervised learning, where video frames are sparsely annotated. With the motivation that the vast amount of video data on the Internet only comes with meta-tags or content keywords that do not provide any temporal ordering information, we finally propose a method for action segmentation that learns from unordered sets of actions only. All approaches are evaluated on several commonly used benchmark datasets. With the proposed methods, we reach state-of-the-art performance for both, fully and weakly supervised action segmentation

A benchmark of dynamic versus static methods for facial action unit detection

Action Units activation is a set of local individual facial muscle parts that occur in time constituting a natural facial expression event. AUs occurrence activation detection can be inferred as temporally consecutive evolving movements of these parts. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. Our work is divided into three contributions: first, we extracted the features from Local Binary Patterns, Local Phase Quantisation, and dynamic texture descriptor LPQTOP with two distinct leveraged network models from different CNN architectures for local deep visual learning for AU image analysis. Second, cascading the LPQTOP feature vector with Long Short-Term Memory is used for coding longer term temporal information. Next, we discovered the importance of stacking LSTM on top of CNN for learning temporal information in combining the spatially and temporally schemes simultaneously. Also, we hypothesised that using an unsupervised Slow Feature Analysis method is able to leach invariant information from dynamic textures. Third, we compared continuous scoring predictions between LPQTOP and SVM, LPQTOP with LSTM, and AlexNet. A competitive substantial performance evaluation was carried out on the Enhanced CK dataset. Overall, the results indicate that CNN is very promising and surpassed all other method

Per-exemplar analysis with MFoM fusion learning for multimedia retrieval and recounting

As a large volume of digital video data becomes available, along with revolutionary advances in multimedia technologies, demand related to efficiently retrieving and recounting multimedia data has grown. However, the inherent complexity in representing and recognizing multimedia data, especially for large-scale and unconstrained consumer videos, poses significant challenges. In particular, the following challenges are major concerns in the proposed research. One challenge is that consumer-video data (e.g., videos on YouTube) are mostly unstructured; therefore, evidence for a targeted semantic category is often sparsely located across time. To address the issue, a segmental multi-way local feature pooling method by using scene concept analysis is proposed. In particular, the proposed method utilizes scene concepts that are pre-constructed by clustering video segments into categories in an unsupervised manner. Then, a video is represented with multiple feature descriptors with respect to scene concepts. Finally, multiple kernels are constructed from the feature descriptors, and then, are combined into a final kernel that improves the discriminative power for multimedia event detection. Another challenge is that most semantic categories used for multimedia retrieval have inherent within-class diversity that can be dramatic and can raise the question as to whether conventional approaches are still successful and scalable. To consider such huge variability and further improve recounting capabilities, a per-exemplar learning scheme is proposed with a focus on fusing multiple types of heterogeneous features for video retrieval. While the conventional approach for multimedia retrieval involves learning a single classifier per category, the proposed scheme learns multiple detection models, one for each training exemplar. In particular, a local distance function is defined as a linear combination of element distance measured by each features. Then, a weight vector of the local distance function is learned in a discriminative learning method by taking only neighboring samples around an exemplar as training samples. In this way, a retrieval problem is redefined as an association problem, i.e., test samples are retrieved by association-based rules. In addition, the quality of a multimedia-retrieval system is often evaluated by domain-specific performance metrics that serve sophisticated user needs. To address such criteria for evaluating a multimedia-retrieval system, in MFoM learning, novel algorithms were proposed to explicitly optimize two challenging metrics, AP and a weighted sum of the probabilities of false alarms and missed detections at a target error ratio. Most conventional learning schemes attempt to optimize their own learning criteria, as opposed to domain-specific performance measures. By addressing this discrepancy, the proposed learning scheme approximates the given performance measure, which is discrete and makes it difficult to apply conventional optimization schemes, with a continuous and differentiable loss function which can be directly optimized. Then, a GPD algorithm is applied to optimizing this loss function.Ph.D