Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features

Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.Comment: 5 page

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.Comment: 14 pages, 7 figures, 11 table

Periocular in the Wild Embedding Learning with Cross-Modal Consistent Knowledge Distillation

Periocular biometric, or peripheral area of ocular, is a collaborative alternative to face, especially if a face is occluded or masked. In practice, sole periocular biometric captures least salient facial features, thereby suffering from intra-class compactness and inter-class dispersion issues particularly in the wild environment. To address these problems, we transfer useful information from face to support periocular modality by means of knowledge distillation (KD) for embedding learning. However, applying typical KD techniques to heterogeneous modalities directly is suboptimal. We put forward in this paper a deep face-to-periocular distillation networks, coined as cross-modal consistent knowledge distillation (CM-CKD) henceforward. The three key ingredients of CM-CKD are (1) shared-weight networks, (2) consistent batch normalization, and (3) a bidirectional consistency distillation for face and periocular through an effectual CKD loss. To be more specific, we leverage face modality for periocular embedding learning, but only periocular images are targeted for identification or verification tasks. Extensive experiments on six constrained and unconstrained periocular datasets disclose that the CM-CKD-learned periocular embeddings extend identification and verification performance by 50% in terms of relative performance gain computed based upon face and periocular baselines. The experiments also reveal that the CM-CKD-learned periocular features enjoy better subject-wise cluster separation, thereby refining the overall accuracy performance.Comment: 30 page

Offline Handwritten Signature Watermark for Digital Document Authentication

This thesis introduces a novel biometric watermarking technique using offline handwritten signature, namely, signature image, as the watermark. Traditionally, the host image is embedded using originator\'s name, graphical logo and serial number which can easily be forged. Biometric watermarking, which synergistically integrates biometrics and the digital watermarking technology, is therefore proposed to replace the traditional digital watermarking techniques. The primary advantage of the offline handwritten is that it is a type of biometric behavioral attribute possessing unique means to identify an individually unambiguously

Solutions to the Diophantine Equation x2 + 16 ∙ 7b = y2r

We present a method of determining integral solutions to the equation x2 + 16 ∙ 7b = y2r, where x, y, b, r ∈ ℤ+. We observe that the results can be classified into several categories. Under each category, a general formula is obtained using the geometric progression method. We then provide the bound for the number of non-negative integral solutions associated with each b. Lastly, the general formula for each of the categories is obtained and presented to determine the respective values of x and yr. We also highlight two special cases where different formulae are needed to represent their integral solutions. © Copyright Yow. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.11Nscopu

An Implicit Identity-Extended Data Augmentation for Low-Resolution Face Representation Learning

Low-resolution (LR) face recognition (LRFR) tackles tiny face images detected from real-world surveillance camera footage, which are unconstrained and generally poor in quality. Owing to the absence of a million-scale labeled LR face dataset, identity-invariant data augmentation (DA) transformations such as flipping, rotation, rescaling, etc., are applied to inflate the effective training examples with respect to the source identities for representation learning. Unfortunately, the identity-invariant property incurs additional intra-class disparity that impairs generalization performance. In this paper, we put forward a new means of DA strategy, termed identity-extended DA, that satisfies both affinity and diversity requirements essential to DA. We instantiate an implicit identity-extended augmentation network, or simply IDEA-Net, to realize the proposed identity-extended DA for LRFR. More specifically, training an IDEA-Net instance augments the small-scale LR (query) face dataset with identity-extended (auxiliary) face examples implicitly in the representation space. We also introduce a calibrator to regulate the disordered representation space by refining the intra-class compactness and the inter-class separation. This diminishes the distribution shift between the original and the augmented examples (affinity) and increases the learning complexity (diversity). We substantiate that IDEA-Net renders a high affinity and diversity representation space. On the other hand, our experimental results on three real-world LR face datasets demonstrate that IDEA-Nets outperform the baselines and other counterparts trained without leveraging the identity-extended examples for LRFR.11Nsciescopu

Fusion of LSB and DWT biometric watermarking for offline handwritten signature

Biometric watermarking refers to the incorporation of biometrics in watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature invisibly in the host as a notice of legitimate ownership. We propose to adaptively fuse Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT)-based approaches into a unison framework, which to be known as LSB-DWT scheme. The performance of LSB-DWT scheme is validated against simulated frequency and geometric attacks, specifically JPG compression, low pass filtering, median filtering, noise addition, scaling, rotation and cropping through visual inspection, Peak Signal to Noise Ratio (PSNR) and watermark distortion rate. Experiment results prove that LSB-DWT scheme is sufficiently robust even in the presence of deliberate distortions

Fusion of LSB and DWT biometric watermarking using offline handwritten signature for copyright protection

Biometric watermarking was introduced as the synergistic integration of biometrics and digital watermarking technology. This paper proposes a novel biometric watermarking technique, which embeds offline handwritten signature in host image for copyright protection. We propose to combine the conventional LSB-based and DWT-based watermarking techniques into a unison framework, which is known as LSB-DWT in this paper. The proposed LSB-DWT technique is evaluated against various simulated security attacks, i.e. JPEG compression, Gaussian low-pass filtering, median filtering, Gaussian noise, scaling, rotation and cropping. The experimental results demonstrate that the proposed LSB-DWT technique exhibits remarkable watermark imperceptibility and watermark robustness

Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature

Biometric watermarking refers to the incorporation of biometrics and watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature in the host as a notice of legitimate ownership. The core of the proposed method is the synergistic integration of a statistical classifier, i.e. the Support Vector Machine, with biometric watermarking to precisely extract the signature code from the host. We abbreviate the proposed method as SVM-BW. The performance of SVM-BW is validated against simulated frequency and geometric attacks, which include JPG compression, low pass filtering, median filtering, noise addition, scaling, rotation and cropping. Experiment results reveal that SVM-BW is able to endure severe degradation on the host fidelity. Furthermore, SVM-BW shows remarkable robustness even if the host is deliberately distorted