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

Global And Local ◽

Sparse Features ◽

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.

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 ◽

Future Work ◽

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.

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 Using Back Propagation Neural Network and Fourier Gabor Filters

Signal & Image Processing An International Journal ◽

10.5121/sipij.2011.2302 ◽

2011 ◽

Vol 2 (3) ◽

pp. 15-21 ◽

Cited By ~ 3

Author(s):

Anissa Bouzalmat ◽

Naouar Belghini ◽

Arsalane Zarghili ◽

Jamal Kharroubi

Keyword(s):

Neural Network ◽

Face Detection ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Gabor Filters ◽

Designing a new recurrent convolutional neural network for face detection and recognition in a color image

Iran Journal of Computer Science ◽

10.1007/s42044-020-00076-w ◽

2021 ◽

Author(s):

Jafar Tavoosi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Face Detection ◽

Color Image ◽

Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

Journal of Information Technology ◽

10.47292/joint.v3i1.45 ◽

2021 ◽

Vol 3 (1) ◽

pp. 33-38

Author(s):

Febiannisa Utami ◽

Suhendri Suhendri ◽

Muhammad Abdul Mujib

Keyword(s):

Face Detection ◽

Detection System ◽

Image Data ◽

Image Process ◽

Cascade Algorithm ◽

Ip Camera ◽

The Face ◽

Haar Cascade ◽

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition

Neural Networks and 3D Edge Genetic Template Matching for Real-Time Face Detection and Recognition

Artificial Intelligence and Integrated Intelligent Information Systems ◽

10.4018/978-1-59904-249-7.ch009 ◽

2011 ◽

pp. 164-177

Author(s):

Stephen Karungaru ◽

Minoru Fukumi ◽

Norio Akamatsu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Genetic Algorithms ◽

Real Time ◽

Face Detection ◽

Template Matching ◽

Neural Network Learning ◽

The One ◽

This chapter describes a novel system that can track and recognize faces in real time using neural networks and genetic algorithms. The main feature of this system is a 3D facemask that combined with a neural network based face detector and adaptive template matching using genetic algorithms, is capable of detecting and recognizing faces in real time. Neural network learning and template matching enable size and pose invariant face detection and recognition while the genetic algorithm optimizes the searching algorithms enabling real time usage of the system. It is hoped that this chapter will show how and why neural networks and genetic algorithms are well suited to solve complex pattern recognition problems like the one presented in this chapter.