62548937
Performance Modeling and Prediction of Face Recognition Systems
It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods.
52809451
Improving Face Recognition by Online Image Alignment
Face recognition accuracy is affected by many factors. This paper studies one of the factors, and provides a reliable image alignment for face recognition. For this purpose, a performance metric is extracted from an analysis of face recognition similarity scores. The metric varies with face alignment, and has a relationship with the actual recognition accuracy. Our method adjusts face alignment online by selecting an alignment candidate corresponding to the largest performance metric. The experimental results show that the presented method can improve the accuracy and robustness of current face recognition systems
14807870
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition", based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods
26841509
From inheritance relation to nonaxiomatic logic
Abstract A nonaxiomatic reasoning system is an adaptive system that works with insufficient knowledge and resources. At the beginning of the paper, three binary term logics are defined. The first is based only on an inheritance relation. The second and the third suggest a novel way to process extension and intension, and they also have interesting relations with Aristotle's syllogistic logic. Based on the three simple systems, a nonaxiomatic logic is defined. It has a term-oriented language and an experience-grounded semantics. It can uniformly represent and process randomness, fuzziness, and ignorance. It can also uniformly carry out deduction, abduction, induction, and revision.
13452585
Robust Clustering based on Winner-Population Markov Chain
In this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by Markov chain modeling. Selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. The experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiency
14294426
Gigabit packet classification by using lookahead caching
Hashing is a widely used method to perform fast lookup. Several schemes have been proposed to support Internet lookup that includes IP lookup and packet classification. Rectangular search is a well-known packet classification scheme based on multiple hash accesses for different filter length. It shows good scalability with respect to the number of filters; however, the lookup performance is not satisfactory. For example, through experiments, each packet classification takes about 40 hash accesses in a 100,000-filter database and each hash access may take more than one memory access. Obviously, this is insufficient to provide gigabits throughput. We proposed a novel "lookahead caching" which can significantly improve the performance of the hash-based algorithm. The basic idea is to find out the unmatched case for each incoming packet, thus it is different from the traditional caching mechanism. The experimental results indicate that the proposed scheme can improve the performance by a factor of two. The scheme can be further enhanced using parallel processing.
16442037
Handwritten Bank Check Recognition Of Courtesy Amounts
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates.
42651
Analytical design of maximally flat FIR fractional Hilbert transformers
Recently, the classical Hilbert transformer is generalized into the fractional Hilbert transformer which could be implemented optically. This modification of the Hilbert transform adds an additional degree of freedom on the Hilbert transformer and improves the performance of the transform. In this paper, the design of FIR filters fractional Hilbert transformer is proposed. The FIR filters are designed in the maximally flat sense. The impulse responses of the filters are uniquely solved and expressed in simple analytic forms. The impulse responses can be exactly expressed as fixed point binary values. The resulting frequency responses approximate the ideal one very well in the middle-frequency band. Efficient hardware realization structures are obtained based on the symmetric properties of the impulse responses. Several design examples with various transform parameters and various filter orders are presented. Some examples of 1-D/2-D edge detection are given. The examples show that the proposed FIR filter can enhance the selected edges very efficiently.