40,222 research outputs found
Automatic human face detection for content-based image annotation
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity
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
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
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