Andrew Abel

Biometric Applications Related to Human Beings: There Is Life beyond Security

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information.

A New Spatio-Temporal Saliency-Based Video Object Segmentation

Humans and animals are able to segment visual scenes by having the natural cognitive ability to quickly identify salient objects in both static and dynamic scenes. In this paper, we present a new spatio-temporal-based approach to video object segmentation that considers both motion- and image-based saliency to produce a weighted approach which can segment both static and dynamic objects. We perform fast optical flow and then calculate the motion saliency based on this temporal information, detecting the presence of global motion and adjusting the initial optical flow results accordingly. This is then fused with a region-based contrast image saliency method, with both techniques weighted. Finally, our joint weighted saliency map is used as part of a foreground–background labelling approach to produce the final segmented video files. Good results in a wide range of environments are presented, showing that our spatio-temporal system is more robust and consistent than a number of other state-of-the-art approaches.

Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation

In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, a state of the art Semi Adaptive Appearance Model (SAAM) approach developed by the authors is used for automatic lip tracking, and an adapted version of our vowel based speech segmentation system is employed to automatically segment speech. Canonical Correlation Analysis (CCA) on segmented and non segmented data in a range of noisy speech environments finds that segmented speech has a significantly better audiovisual correlation, demonstrating the feasibility of our techniques for further development as part of a proposed audiovisual speech enhancement system.

A Power-Efficient Capacitive Read-Out Circuit With Parasitic-Cancellation for MEMS Cochlea Sensors

This paper proposes a solution for signal read-out in the MEMS cochlea sensors that have very small sensing capacitance and do not have differential sensing structures. The key challenge in such sensors is the significant signal degradation caused by the parasitic capacitance at the MEMS-CMOS interface. Therefore, a novel capacitive read-out circuit with parasitic-cancellation mechanism is developed; the equivalent input capacitance of the circuit is negative and can be adjusted to cancel the parasitic capacitance. Chip results prove that the use of parasitic-cancellation is able to increase the sensor sensitivity by 35 dB without consuming any extra power. In general, the circuit follows a low-degradation low-amplification approach which is more power-efficient than the traditional high-degradation high-amplification approach; it employs parasitic-cancellation to reduce the signal degradation and therefore a lower gain is required in the amplification stage. Besides, the chopper-stabilization technique is employed to effectively reduce the low-frequency circuit noise and DC offsets. As a result of these design considerations, the prototype chip demonstrates the capability of converting a 7.5 fF capacitance change of a 1-Volt-biased 0.5 pF capacitive sensor pair into a 0.745 V signal-conditioned output at the cost of only 165.2 μW power consumption.

A local-global mixed kernel with reproducing property

A wide variety of kernel-based methods have been developed with great successes in many fields, but very little research has focused on the reproducing kernel function in Reproducing Kernel Hilbert Space (RKHS). In this paper, we propose a novel method which we call a local-global mixed kernel with reproducing property (LGMKRP) to successfully perform a range of classification tasks in the RKHS rather than the more conventionally used Hilbert space. The LGMKRP proposed in this paper consists of two major components. First, we find the basic solution of a generalized differential operator by the delta function, and prove that this basic solution is a new specific reproducing kernel called a local H-reproducing kernel (LHRK) in RKHS. This reproducing kernel has good local properties, including odd order vanishing moment, and fast dilation attenuation. Second, in the RKHS, we prove that the LHRK satisfies the condition of Mercers theorem, and prove that it is a typical polynomial kernel with global property, which also possesses the reproducing property. Furthermore, the novel specific mixed kernel (i.e., LGMKRP) proposed in this paper is based on these two different properties. Experimental results demonstrate that the LGMKRP possesses the approximation and regularization performance of a reproducing kernel, and can enhance the generalization ability of kernel methods. We find the basic solution of a generalized differential operator, and prove that this basic solution is a new specific reproducing kernel.We prove that the local H-reproducing kernel satisfies the condition of Mercer kernel.We prove that the typical polynomial kernel with global property possesses reproducing property.We define a novel method named local-global mixed kernel with reproducing property.We evaluate the performance of our mixed kernel on standard UCI datasets.We demonstrate the effectiveness of the proposed mixed kernel.

Novel Two-Stage Audiovisual Speech Filtering in Noisy Environments

Abstract In recent years, the established link between the various human communication production domains has become more widely utilised in the field of speech processing. In this work, we build on previous work by the authors and present a novel two-stage audiovisual speech enhancement system, making use of audio-only beamforming, automatic lip tracking, and pre-processing with visually derived Wiener speech filtering. Initial results have demonstrated that this two-stage multimodal speech enhancement approach can produce positive results with noisy speech mixtures that conventional audio-only beamforming would struggle to cope with, such as in very noisy environments with a very low signal to noise ratio, and when the type of noise is difficult for audio-only beamforming to process.

An Investigation into Audiovisual Speech Correlation in Reverberant Noisy Environments

As evidence of a link between the various human communication production domains has become more prominent in the last decade, the field of multimodal speech processing has undergone significant expansion. Many different specialised processing methods have been developed to attempt to analyze and utilize the complex relationship between multimodal data streams. This work uses information extracted from an audiovisual corpus to investigate and assess the correlation between audio and visual features in speech. A number of different feature extraction techniques are assessed, with the intention of identifying the visual technique that maximizes the audiovisual correlation. Additionally, this paper aims to demonstrate that a noisy and reverberant audio environment reduces the degree of audiovisual correlation, and that the application of a beamformer remedies this. Experimental results, carried out in a synthetic scenario, confirm the positive impact of beamforming not only for improving the audio-visual correlation but also in a complete audio-visual speech enhancement scheme. Thus, this work inevitably highlights an important aspect for the development of future promising bimodal speech enhancement systems.

Flexible and Creative Chinese Poetry Generation Using Neural Memory.

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.

Reversible data hiding in encrypted images based on multi-level encryption and block histogram modification

In recent years there has been significant interest in reversible data hiding, and also in particular, reversible data hiding in encrypted images (RDH-EI). This means that additional data can be embedded into a previously encrypted image with no knowledge of the original image content. According to the held keys, legal receivers can get the embedded data or an image very similar to the original one, or, both the embedded data and an image exactly as the original one. In this paper, we propose and evaluate a RDH-EI framework. Firstly, we propose a multi-level encryption (MLE) scheme using both Josephus traversal based multi-granular encryption and a stream cipher. To reduce the quantity of side information required to embed into images together with additional data, we also present a block histogram modification (BHM) approach with self-hidden peak pixels to perform reversible data embedding and a location map marking scheme to perform histogram contraction and recovery. The experimental results demonstrate that, in comparison with other similar methods, the proposed framework achieves improvements in terms of the embedding payload, the decrypted image quality and the accuracy of image restoration.

Reversible data hiding in encrypted image based on block histogram shifting

Since there is good potential for practical applications such as encrypted image authentication, content owner identification and privacy protection, reversible data hiding in encrypted image (RDHEI) has attracted increasing attention in recent years. In this paper, we propose and evaluate a new separable RDHEI framework. Additional data can be embedded into a cipher image previously encrypted using Josephus traversal and a stream cipher. A block histogram shifting (BHS) approach using self-hidden peak pixels is adopted to perform reversible data embedding. Depending on the keys held, legal receivers can extract only the embedded data with the data hiding key, or, they can decrypt an image very similar to the original with the decryption key. They can extract both the embedded data and recover the original image error-free if both keys are available. The results demonstrate that higher embedding payload, better quality of decrypted-marked image and error-free image recovery are achieved.