For digital imagery, face detection and identification are functions of great importance in wide-ranging applications, including full facial recognition systems. The development and evaluation of unique and existing face detection and face identification applications require a significant amount of data. Increased availability of such data volumes could benefit the formulation and advancement of many biometric algorithms. Here, the utility of using synthetically generated face data to evaluate facial biometry methodologies to a precision that would be unrealistic for a parametrically uncontrolled dataset, is demonstrated. Particular attention is given to similarity metrics, symmetry within and between recognition algorithms, discriminatory power and optimality of pan and/or tilt in reference images or libraries, susceptibilities to variations, identification confidence, meaningful identification mislabelings, sensitivity, specificity, and threshold values. The face identification results, in particular, could be generalized to address shortcomings in various applications and help to inform the design of future strategies.
Proc. SPIE. 9124, Satellite Data Compression, Communications, and Processing X
KEYWORDS: Hyperspectral imaging, Sensors, Image processing, Data transmission, Data processing, Signal processing, Satellite communications, Electrical engineering, Algorithm development, Data communications
This paper presents a progressive band processing (PBP) version of an endmember finding algorithm, simplex growing algorithm (SGA), to be called PBP-SGA, which allows users to perform SGA band by band progressively. Several advantages can be gained from this approach. First of all, PBP-SGA does not require data dimensionality reduction since PBP begins with a lower band dimension and gradually increases band dimensions band by band progressively until it achieves the desired results. Secondly, PBP can process SGA whenever bands are available without waiting for the information from all band information to be received. As a result, PBP-SGA can be used for data transmission and communication. Thirdly, PBP-SGA can help identify which bands are crucial during the process of finding endmembers. Finally, PBP-SGA provides feasibility of being implemented in real time according to Band SeQuential (BSQ) format.
Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.
Band selection (BS) has advantages over data dimensionality in satellite communication and data transmission in the sense that spectral bands can be selected by users at their discretion for data analysis, while preserving data fidelity. However, to materialize BS in such practical applications several issues need to be addressed. One is how many bands required for BS. Another is how to select appropriate bands. A third one is how to take advantage of previously selected bands without re-implementing BS. Finally and most important one is how to process BS as number of bands varies. This paper presents a specific application to progressive band processing of anomaly detection, which does not require BS and can be carried out in a progressive fashion with data updated recursively band by band in the same way that data is processed by a Kalman filter.
Proc. SPIE. 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
KEYWORDS: Target detection, Hyperspectral imaging, Detection and tracking algorithms, Sensors, Image processing, Data processing, Spatial resolution, Electrical engineering, Communication engineering, Real time processing algorithms
Constrained energy minimization (CEM) has been widely used for subpixel detection. It makes use of the sample correlation matrix R by suppressing the background thus enhancing detection of targets of interest. In many real world problems, implementing target detection on a timely basis is crucial, specifically moving targets. However, since the calculation of the sample correlation matrix R needs the complete data set prior to its use in detection, CEM is prevented from being implemented as a real time processing algorithm. In order to resolve this dilemma, the sample correlation matrix R must be replaced with a causal sample correlation matrix formed by only those data samples that have been visited and the currently being processed data sample. This causality is a pre-requisite to real time processing. By virtue of such causality, designing and developing a real time processing version of CEM becomes feasible. This paper presents a progressive CEM (PCEM) where the causal sample correlation matrix can be updated sample by sample. Accordingly, PCEM allows the CEM to be implemented as a causal CEM (C-CEM) as well as real time (RT) CEM via a recursive update equation in real time.
With improved smartphone and tablet technology, it is becoming increasingly feasible to implement powerful biometric
recognition algorithms on portable devices. Typical iris recognition algorithms, such as Ridge Energy Direction (RED),
utilize two-dimensional convolution in their implementation. This paper explores the energy consumption implications
of 12 different methods of implementing two-dimensional convolution on a portable device. Typically, convolution is
implemented using floating point operations. If a given algorithm implemented integer convolution vice floating point
convolution, it could drastically reduce the energy consumed by the processor. The 12 methods compared include 4
major categories: Integer C, Integer Java, Floating Point C, and Floating Point Java. Each major category is further
divided into 3 implementations: variable size looped convolution, static size looped convolution, and unrolled looped
convolution. All testing was performed using the HTC Thunderbolt with energy measured directly using a Tektronix
TDS5104B Digital Phosphor oscilloscope. Results indicate that energy savings as high as 75% are possible by using
Integer C versus Floating Point C. Considering the relative proportion of processing time that convolution is responsible
for in a typical algorithm, the savings in energy would likely result in significantly greater time between battery charges.