Detección y Seguimiento Facial en Niños Autistas con Bajo Nivel de Funcionamiento: Face detection and face tracking in autistics children with low functioning

2013 ◽  
Author(s):  
Y. Castro ◽  
J. D. Posada ◽  
J. A. Villanueva ◽  
J. C. Bejarano
Keyword(s):  
Face Detection ◽  
Face Tracking ◽  
Low Functioning

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

Face Tracking

2011 ◽  
pp. 92-162
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Image Sequence ◽  
Face Tracking ◽  
Detection Methods ◽  
Continuous Change ◽  
Image Frame ◽  
The Face ◽  
Previous Image

When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous image frames with an assumption that the shape, texture, or motion of the face change smoothly. There have been many approaches to face tracking. We divide the face tracking methods into several categories according to the cues that are extracted for tracking.

Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

2013 ◽  
Vol 373-375 ◽  
pp. 442-446
Author(s):  
Hai Feng Sang ◽  
Chao Xu ◽  
Dan Yang Wu ◽  
Jing Huang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Research Field ◽  
Face Tracking ◽  
Fusion Algorithm ◽  
The Real ◽  
Video Images ◽  
Tracking And Recognition

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

scholarly journals A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database

2019 ◽  
Vol 892 ◽  
pp. 31-37 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O.K. Rahmat ◽  
Hizmawati Bint Madzin
Keyword(s):  
Facial Expressions ◽  
Face Detection ◽  
Detection Method ◽  
Detection System ◽  
Face Tracking ◽  
Skin Colour ◽  
Content Based Image Retrieval ◽  
Expression Recognition ◽  
World Image ◽  
Indoor And Outdoor

Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition, face tracking, face identification, expression recognition and also content-based image retrieval. A potent face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, orientation and facial expressions. In previous literature, many approaches for face detection had been proposed. However, face detection in outdoor images with uncontrolled illumination and images with complex background are still a serious problem. Hence, in this paper, we had proposed a Geometric Skin Colour (GSC) method for detecting faces accurately in real world image, under capturing conditions of both indoor and outdoor, and with a variety of illuminations and also in cluttered backgrounds. The selected method was evaluated on two different face video smartphone databases and the obtained results proved the outperformance of the proposed method under the unconstrained environment of these databases.

scholarly journals Face Tracking Performance in Head Gesture Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 1096-1099
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Face Detection ◽  
Gesture Recognition ◽  
Recognition System ◽  
Extraction Methods ◽  
Face Tracking ◽  
Tracking Performance ◽  
Adaboost Algorithm ◽  
Camshift Algorithm

This paper describes the comparative analysis of different face tracking methods in the head gesture recognition system. The major constraints of head gesture recognition system, i.e. face detection, feature extraction, tracking, and recognition are explained. We used adaboost algorithm for detection, and Camshift algorithm for tracking with different feature extraction methods. We performed extensive experimentations and presented a comparative analysis of tracking performance of head gesture recognition system under cluttered backgrounds, shadow and sunshine conditions. Experimental results show the robustness in face detection, tracking and direction recognition of the proposed method.

scholarly journals Smart Surveillance System using Face Tracking

2021 ◽  
Vol 9 (VI) ◽  
pp. 2613-2617
Author(s):  
Priyanka Agrawal
Keyword(s):  
Face Detection ◽  
Criminal Activity ◽  
Video Tracking ◽  
Face Tracking ◽  
Surveillance Systems ◽  
Video Analytics ◽  
Detection And Tracking ◽  
The Face ◽  
Video Input ◽  
Human Faces

The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.

scholarly journals Aplikasi Try-On Hairstyle Berbasis Augmented Reality

2020 ◽  
Vol 19 (02) ◽  
pp. 55-70
Author(s):  
Pinasthika Aulia Fadhila ◽  
Ledya Novamizanti ◽  
Fat’hah Noor Prawita
Keyword(s):  
Augmented Reality ◽  
Face Detection ◽  
User Acceptance ◽  
Black Box ◽  
Face Tracking

Augmented Reality merupakan teknologi yang dapat mempermudah hidup manusia yang diaplikasikan di berbagai macam bidang seperti pengelola jasa pangkas rambut. Ketidakpastian pemilihan model rambut oleh pelanggan membuat penata rambut dan pelanggan menjadi ragu. Try-On Hairstyle merupakan aplikasi berbasis android yang dapat memberikan gambaran secara virtual berbagai jenis model rambut pria maupun wanita yang sesuai dengan bentuk wajah pengguna. Aplikasi ini menerapkan algoritma Viola-Jones untuk tahap face detection dan face tracking secara markerless. Setelah melakukan deteksi wajah, sistem akan mengkategorikan wajah pelanggan berdasarkan bentuk wajah dan aplikasi ini akan menampilkan berbagai model rambut yang tersedia berdasarkan bentuk wajah pelanggan dengan teknologi augmented reality. Sistem ini diuji dengan pengujian black box, pengujian berdasarkan jarak, pengujian berdasarkan intensitas cahaya, pengujian berdasarkan rotasi kepala, dan pengujian user acceptance. Hasil dari aplikasi ini adalah dapat mendeteksi wajah dan menampilkan model rambut yang sesuai dengan akurasi 100% dengan jarak terbaik pada 30 cm-40 cm, sudut wajah terhadap kamera sebesar 0°, dan cahaya dengan intensitas cahaya lebih besar dari 10 lux. Tingkat kepuasan pelanggan dalam menggunakan aplikasi ini sebesar 91,7625%.

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