Machine Vision Application on Science and Industry

Face detection, tracking and recognition is still actual field of human centered technologies used for developing more natural communication between computing artefacts and users. Analyzing modern trends and advances in this field, two approaches for face sensing and recognition have been proposed. The first color/shape-based approach uses sets of fuzzy saturated color regions providing face detection by Fourier descriptors and recognition by SVM. The second approach provides fast face detection by adaptive boosting algorithm, and recognition based on SIFT key point extraction into eye-nose-mouth regions has been improved using Bayesian approach. Designed systems have been tested in order to evaluate capability of the proposed approaches to detect, trace and interpret faces of known individuals registered into facial standard databases providing correct recognition rate in range of 94.5-99.0% with recall up to 46%. The conducted tests ensure that both approaches have satisfactory performance achieving less than 3 seconds for human face detection and recognition in live video streams.

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Development of a Tool for Assisting Group Conversation by Re-Voicing Supportive Responses

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.446-447.1040 ◽

2013 ◽

Vol 446-447 ◽

pp. 1040-1044 ◽

Cited By ~ 2

Author(s):

Myagmarbayar Nergui ◽

Mihoko Otake

Keyword(s):

Young Adults ◽

Cognitive Function ◽

Source Localization ◽

Sound Source ◽

Recognition Rate ◽

Sound Source Localization ◽

Sensory Environment ◽

Face Detection And Recognition ◽

Correct Recognition Rate ◽

Detection And Recognition

Interactive group conversation is very important for improvement of cognitive function and prevention of dementia. Interactive means that all the participants participate equally and actively into group conversation. How to make group conversation more interactive for all participants is a challenging task. Main purpose of this study is to develop the system that assists group conversation, which is capable of dealing with following things, face detection and recognition, speech certain contents recognition, repetition or re-voice of supportive responses based on the result of speech contents recognition, and sound source localization using less sensory environment. We did dialogue experiments with arbitrary pairs among seven young adults and analyzed 14 dialogue data for calculating correct recognition rate of face and speech certain contents, for synthesizing speech based on the result of recognition, and for calculating sound source. The performance of the developed system was preliminary verified through the experimental results. The developed system can be used as a basis of assisting group conversation.

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Security System Design Based on Human Face Detection and Recognition on Android Platform

IJIREEICE ◽

10.17148/ijireeice.2015.3733 ◽

2015 ◽

Vol 3 (7) ◽

pp. 152-157

Author(s):

Sheshmal S. Shingne ◽

Krishnamurthy V

Keyword(s):

System Design ◽

Face Detection ◽

Security System ◽

Human Face ◽

Human Face Detection ◽

Android Platform ◽

Face Detection And Recognition ◽

Detection And Recognition

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An Efficient Method for Multiple Face Detection & Recognition

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1086.1291s319 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 474-478

Keyword(s):

Face Recognition ◽

Face Detection ◽

Efficient Method ◽

Recognition Rate ◽

The Future ◽

Web Camera ◽

The Face ◽

Face Detection And Recognition ◽

Future Work ◽

Detection And Recognition

The problem of Face detection and recognition is becoming a challenge due to the wide variety of faces and the complexity of the noises and background of image. In this paper we have used C-sharp and Haar algorithm to detect the face. First in this paper the image is taken with a web-camera, storing it in the database and then once again when the person comes in the frame the name of the person is displayed. This paper is done in C-sharp which was a bit difficult for us to do and we have combined both the face detection and the recognition. The proposed method has good output and a good recognition rate. The limitation of the paper is that it does not display the name of the person above the face. In the future work will be carried on the above said topic. While developing the code some sample codes in python but those were basic programs. So this paper aims to find a solution for it and developed in C-sharp. In finding the XML file of haarcascade frontal face detection we found some problems and had to do a bit of research in finding it. The code for face detection and face recognition were found in different places and in this paper the codes for the both has been combined and found some difficulty. To overcome the basic programs we have written the code in C-sharp and the difficulty which we faced in combining the two codes have been solved. The solution has been successfully implemented and the code is fully running and the output has been successfully achieved.

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MULTI-STEP FACE RECOGNITION FOR IMPROVING FACE DETECTION AND RECOGNITION RATE

Far East Journal of Electronics and Communications ◽

10.17654/ec016030471 ◽

2016 ◽

Vol 16 (3) ◽

pp. 471-491 ◽

Cited By ~ 6

Author(s):

Aigerim Altayeva ◽

Batyrkhan Omarov ◽

Hyeong Chul Jeong ◽

Young Im Cho

Keyword(s):

Face Recognition ◽

Face Detection ◽

Recognition Rate ◽

Face Detection And Recognition ◽

Detection And Recognition

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Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

10.36227/techrxiv.16698940 ◽

2021 ◽

Author(s):

Islem Jarraya ◽

Wael Ouarda ◽

Fatma BenSaid ◽

Adel Alimi

Keyword(s):

Neural Network ◽

Face Detection ◽

Detection System ◽

Recognition Rate ◽

Biometric System ◽

Training Time ◽

Face Detection And Recognition ◽

Global And Local ◽

Sparse Features ◽

Detection And Recognition

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

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