34,830 research outputs found
Automated Students Attendance System
The Automated Students' Attendance System is a system that takes the attendance of
students in a class automatically. The system aims to improve the current attendance
system that is done manually. This work presents the computerized system of automated
students' attendance system to implement genetic algorithms in a face recognition
system. The extraction of face template particularly the T-zone (symmetrical between
the eyes, nose and mouth) is performed based on face detection using specific HSV
colour space ranges followed by template matching. Two types of templates are used;
one on edge detection and another on the intensity plane in YIQ colour space. Face
recognition with genetic algorithms will be performed to achieve an automated students'
attendance system. With the existence of this attendance system, the occurrence of
truancy could be reduced tremendously
One-to-many face recognition with bilinear CNNs
The recent explosive growth in convolutional neural network (CNN) research
has produced a variety of new architectures for deep learning. One intriguing
new architecture is the bilinear CNN (B-CNN), which has shown dramatic
performance gains on certain fine-grained recognition problems [15]. We apply
this new CNN to the challenging new face recognition benchmark, the IARPA Janus
Benchmark A (IJB-A) [12]. It features faces from a large number of identities
in challenging real-world conditions. Because the face images were not
identified automatically using a computerized face detection system, it does
not have the bias inherent in such a database. We demonstrate the performance
of the B-CNN model beginning from an AlexNet-style network pre-trained on
ImageNet. We then show results for fine-tuning using a moderate-sized and
public external database, FaceScrub [17]. We also present results with
additional fine-tuning on the limited training data provided by the protocol.
In each case, the fine-tuned bilinear model shows substantial improvements over
the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a
large face database, the recently released VGG-Face model [20], can be
converted into a B-CNN without any additional feature training. This B-CNN
improves upon the CNN performance on the IJB-A benchmark, achieving 89.5%
rank-1 recall.Comment: Published version at WACV 201
Fast Multi-Scale Face Detection
Computerized human face processing (detection, recognition, synthesis) has known an intense research activity during the last few years. Applications involving human face recognition are very broad with an important commercial impacts. Human face processing is a difficult and challenging task: the space of different facial patterns is huge. The variability of human faces as well as their similarity and the influence of other features like beard, glasses, hair, illumination, background etc., make face recognition or face detection difficult to tackle. The main task during the internship was to study and implement a neural-network based face detection algorithm for general scenes, which has previously been developed within the IDIAP Computer Vision group. It also included the study and design of a multi-scale face detection method. A face database and a camera were available to make tests and perform some benchmarking. The main constaint was to have a real-time or almost real-time face detection system. This has beeen achieved. Evaluation of the face detection capability of the employed neural networks were demonstrated on a variety of still images. In addition, we introdudced an efficient preprocessing stage and a new post-processing strategy to eliminate false detections significantly. This allowed to deploy a single neural network for face detection running in a sequential manner on a standard workstation
Automated Students Attendance System
The Automated Students' Attendance System is a system that takes the attendance of
students in a class automatically. The system aims to improve the current attendance
system that is done manually. This work presents the computerized system of automated
students' attendance system to implement genetic algorithms in a face recognition
system. The extraction of face template particularly the T-zone (symmetrical between
the eyes, nose and mouth) is performed based on face detection using specific HSV
colour space ranges followed by template matching. Two types of templates are used;
one on edge detection and another on the intensity plane in YIQ colour space. Face
recognition with genetic algorithms will be performed to achieve an automated students'
attendance system. With the existence of this attendance system, the occurrence of
truancy could be reduced tremendously
Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
Facial wrinkle is one of the most prominent biological changes that
accompanying the natural aging process. However, there are some external
factors contributing to premature wrinkles development, such as sun exposure
and smoking. Clinical studies have shown that heavy smoking causes premature
wrinkles development. However, there is no computerised system that can
automatically assess the facial wrinkles on the whole face. This study
investigates the effect of smoking on facial wrinkling using a social habit
face dataset and an automated computerised computer vision algorithm. The
wrinkles pattern represented in the intensity of 0-255 was first extracted
using a modified Hybrid Hessian Filter. The face was divided into ten
predefined regions, where the wrinkles in each region was extracted. Then the
statistical analysis was performed to analyse which region is effected mainly
by smoking. The result showed that the density of wrinkles for smokers in two
regions around the mouth was significantly higher than the non-smokers, at
p-value of 0.05. Other regions are inconclusive due to lack of large scale
dataset. Finally, the wrinkle was visually compared between smoker and
non-smoker faces by generating a generic 3D face model.Comment: 6 pages, 8 figures, Accepted in 2017 IEEE SMC International
Conferenc
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
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