From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.

High-Speed Tracking with Kernelized Correlation Filters

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

Neural network-based face detection

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

Direct finite element solution on an optical laboratory matrix-vector processor

A new technique is discussed to recognize human faces under varying aspect views (pose). We first estimate the pose of an unknown human face from a 2D gray-scale image and then transform the unknown face to a reference pose using a feature extraction procedure. A different set of features for discriminating between different individuals are then extracted from these reconstructed faces for recognition. The feature extraction scheme used is known as the maximum representation and discrimination feature method. The advantage of our procedure is that it inherently removes distortions due to pose variations, and therefore requires only single training and/or test face images, which could be at different aspect views. For transformation, it does not require the face to be in the database during training. For recognition, only one aspect view at any pose is necessary.

Pose-invariant recognition of faces at unknown aspect views

General expressions are derived for the degradation in signal to noise ratio (SNR) as a function of rotation and scale distortions for modified matched spatial filters (MSFs). These are numerically evaluated for an image class with Gaussian shaped spectral densities to understand the effects of training set size, input noise level and image space bandwidth product (SBWP) on the resulting SNR.

David P. Casasent

Papers

Direct finite element solution on an optical laboratory matrix-vector processor

Pose-invariant recognition of faces at unknown aspect views

Optimality Considerations In Modified Matched Spatial Filters

Accuracy and space bandwidth in space variant optical correlators

Optical pattern recognition for computer vision