Abstract: In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
Abstract: Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: In this paper, a novel system
recognition of human faces without using face
different color photographs is proposed. It mainly in
face detection, normalization and recognition. Foot
method of combination of Haar-like face determined
segmentation and region-based histogram stretchi
(RHST) is proposed to achieve more accurate perf
using Haar. Apart from an effective angle norm
side-face (pose) normalization, which is almost a might be important and beneficial for the prepr
introduced. Then histogram-based and photom
normalization methods are investigated and ada
retinex (ASR) is selected for its satisfactory illumin
Finally, weighted multi-block local binary pattern
with 3 distance measures is applied for pair-mat
Experimental results show its advantageous perfo
with PCA and multi-block LBP, based on a principle.
Abstract: To improve the classification rate of the face
recognition, features combination and a novel non-linear kernel are
proposed. The feature vector concatenates three different radius of
local binary patterns and Gabor wavelet features. Gabor features are
the mean, standard deviation and the skew of each scaling and
orientation parameter. The aim of the new kernel is to incorporate
the power of the kernel methods with the optimal balance between
the features. To verify the effectiveness of the proposed method,
numerous methods are tested by using four datasets, which are
consisting of various emotions, orientations, configuration,
expressions and lighting conditions. Empirical results show the
superiority of the proposed technique when compared to other
methods.
Abstract: The study of the variability of the postural strategies
in low back pain patients, as a criterion in evaluation of the
adaptability of this system to the environmental demands is the
purpose of this study. A cross-sectional case-control study was
performed on 21 recurrent non-specific low back pain patients and 21
healthy volunteers. The electromyography activity of Deltoid,
External Oblique (EO), Transverse Abdominis/Internal Oblique
(TrA/IO) and Erector Spine (ES) muscles of each person was
recorded in 75 rapid arm flexion with maximum acceleration.
Standard deviation of trunk muscles onset relative to deltoid muscle
onset were statistically analyzed by MANOVA . The results show
that chronic low back pain patients exhibit less variability in their
anticipatory postural adjustments (APAs) in comparison with the
control group. There is a decrease in variability of postural control
system of recurrent non-specific low back pain patients that can
result in the persistence of pain and chronicity by decreasing the
adaptability to environmental demands.
Abstract: Single biometric modality recognition is not able to meet the high performance supplies in most cases with its application become more and more broadly. Multimodal biometrics identification represents an emerging trend recently. This paper investigates a novel algorithm based on fusion of both fingerprint and fingervein biometrics. For both biometric recognition, we employ the Monogenic Local Binary Pattern (MonoLBP). This operator integrate the orginal LBP (Local Binary Pattern ) with both other rotation invariant measures: local phase and local surface type. Experimental results confirm that a weighted sum based proposed fusion achieves excellent identification performances opposite unimodal biometric systems. The AUC of proposed approach based on combining the two modalities has very close to unity (0.93).