61,850 research outputs found
Unconstrained face recognition for law enforcement applications
This research work simulates the human visual cortex by using 2D log polar Gabor wavelet to extract the facial features. Scale and orientation independent convolution of face image with Gabor wavelet gives the features in the form of amplitude and phase. The proposed face recognition algorithm is invariant to frequency, scale, filter orientation, illumination, and contrast. We evaluated the recognition algorithm on four face databases namely FERET, CMU AMP, CMU PIE and Notre Dame Face databases. Experimental results show that using single image for training, phase feature based face recognition performs approximately 5% better than amplitude feature based face recognition.;Another facet of this research involves matching scanned and digital face images. Normalization and transformation algorithms are proposed to resample the scanned and the digital images into one common domain. Validation is performed on a face database of 500 classes containing both the scanned and digital face images.;Finally, a synthetic face database is prepared to evaluate the performance of the proposed face recognition algorithm with disguise. The database includes synthetic face images with single and multiple variations in appearance and feature. Results show that the proposed algorithm outperforms other recognition algorithms
Automatic Face Recognition System Based on Local Fourier-Bessel Features
We present an automatic face verification system inspired by known properties
of biological systems. In the proposed algorithm the whole image is converted
from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT).
Using the whole image is compared to the case where only face image regions
(local analysis) are considered. The resulting representations are embedded in
a dissimilarity space, where each image is represented by its distance to all
the other images, and a Pseudo-Fisher discriminator is built. Verification test
results on the FERET database showed that the local-based algorithm outperforms
the global-FBT version. The local-FBT algorithm performed as state-of-the-art
methods under different testing conditions, indicating that the proposed system
is highly robust for expression, age, and illumination variations. We also
evaluated the performance of the proposed system under strong occlusion
conditions and found that it is highly robust for up to 50% of face occlusion.
Finally, we automated completely the verification system by implementing face
and eye detection algorithms. Under this condition, the local approach was only
slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing,
18 (SIBGRAPI
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach
Object recognition in the video sequence or images is one of the sub-field of
computer vision. Moving object recognition from a video sequence is an
appealing topic with applications in various areas such as airport safety,
intrusion surveillance, video monitoring, intelligent highway, etc. Moving
object recognition is the most challenging task in intelligent video
surveillance system. In this regard, many techniques have been proposed based
on different methods. Despite of its importance, moving object recognition in
complex environments is still far from being completely solved for low
resolution videos, foggy videos, and also dim video sequences. All in all,
these make it necessary to develop exceedingly robust techniques. This paper
introduces multiple moving object recognition in the video sequence based on
LoG Gabor-PCA approach and Angle based distance Similarity measures techniques
used to recognize the object as a human, vehicle etc. Number of experiments are
conducted for indoor and outdoor video sequences of standard datasets and also
our own collection of video sequences comprising of partial night vision video
sequences. Experimental results show that our proposed approach achieves an
excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc
Recommended from our members
Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression
Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance
- …