Eye detection in video images with complex Background

Detection of human eye is a significant but difficult task. This paper presents an efficient eye detection approach for video images with complex background. The propose method has two main phases to find eye pair such as locating face and eye region and finding eye. In the first phase the novel approach to fast locating the face and eye region is developed. In the second phase eye finding directed by knowledge is introduced in detail. Both phases developed using Mat lab 7.5. The proposed method is robust against moderated rotations, clustered background, partial face occlusion and glass wearing. We prove the efficiency of our proposed method in detection of eyes complex background i.e. both indoor and outdoor environmen

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Regression Based Gaze Estimation with Natural Head Movement

This thesis presents a non-contact, video-based gaze tracking system using novel eye detection and gaze estimation techniques. The objective of the work is to develop a real-time gaze tracking system that is capable of estimating the gaze accurately under natural head movement. The system contains both hardware and software components. The hardware of the system is responsible for illuminating the scene and capturing facial images for further computer analysis, while the software implements the core technique of gaze tracking which consists of two main modules, i.e., eye detection subsystem and gaze estimation subsystem. The proposed gaze tracking technique uses image plane features, namely, the inter-pupil vector (IPV) and the image center-inter pupil center vector (IC-IPCV) to improve gaze estimation precision under natural head movement. A support vector regression (SVR) based estimation method using image plane features along with traditional pupil center-cornea reflection (PC-CR) vector is also proposed to estimate the gaze. The designed gaze tracking system can work in real-time and achieve an overall estimation accuracy of 0.84º with still head and 2.26º under natural head movement. By using the SVR method for off-line processing, the estimation accuracy with head movement can be improved to 1.12º while providing a tolerance of 10cm×8cm×5cm head movement

Smart attendance monitoring system using computer vision.

Masters Degree. University of KwaZulu-Natal, Durban.Monitoring of student’s attendance remains the fundamental and vital part of any educational institution. The attendance of students to classes can have an impact on their academic performance. With the gradual increase in the number of students, it becomes a challenge for institutions to manage their attendance. The traditional attendance monitoring system requires considerable amount of time due to manual recording of names and circulation of the paper-based attendance sheet for students to sign their names. The paper-based attendance recording method and some existing automated systems such as mobile applications, Radio Frequency Identification (RFID), Bluetooth, and fingerprint attendance models are prone to fake results and time wasting. The limitations of the traditional attendance monitoring system stimulated the adoption of computer vision to stand in the gap. Student’s attendance can be monitored with biometric candidate’s systems such as iris recognition system and face recognition system. Among these, face recognition have a greater potential because of its non-intrusive nature. Although some automated attendance monitoring systems have been proposed, poor system modelling negatively affects the systems. In order to improve success of the automated systems, this research proposes the smart attendance monitoring system that uses facial recognition to monitor student’s attendance in a classroom. A time integrated model is provided to monitor student’s attendance throughout the lecture period by registering the attendance information at regular time intervals. Multi-camera system is also proposed to guarantee an accurate capturing of students. The proposed multi-camera based system is tested using a real-time database in an experimental class from the University of KwaZulu-Natal (UKZN). The results show that the proposed smart attendance monitoring System is reliable, with the average accuracy rate of 98%.Examiner's copy of thesis

Towards an efficient, unsupervised and automatic face detection system for unconstrained environments

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Detección de fatiga en conductores mediante fusión de sistemas ADAS

Se ha identificado la somnolencia como una de las causas más importante de accidentes de tráfico, ya que se encuentra implicada en el 20% de los mismos, por lo que existe un interés creciente en encontrar sistemas ADAS (Advanced Driver Assistance Systems) capaces de detectar el estado de fatiga del conductor para prevenir posibles accidentes. En esta tesis se propone una técnica, basada en el procesado de imágenes monoculares consistente en la detección, seguimiento y caracterización de la apertura de los ojos, que trabaja automáticamente con distintos usuarios y en condiciones de conducción real. A partir de esta información y de otras señales relativas a la conducción, se infiere la somnolencia del conductor. Para la detección de la cara se ha empleado el algoritmo de detección por apariencia de Viola y Jones, y para la de los ojos se ha mejorado con técnicas de clustering y un filtro de Kalman como predictor. La medida de la apertura de los ojos se ha obtenido aplicando filtros adaptativos, integrales proyectivas y un modelo Gaussiano cuya desviación estándar coincide con la apertura, consiguiendo un sistema en tiempo real y robusto frente a cambios de iluminación. Conocida la apertura se calcula el Porcentaje de Ojo Cerrado (PERCLOS), que es uno de los indicadores más importantes en la detección de somnolencia. Todos resultados han sido obtenidos a partir de una amplia colección de vídeos de la cara de diferentes conductores, en simulación y en condiciones reales, en estado normal y de privación de sueño. Los resultados obtenidos sobre la detección de somnolencia demuestran que la utilización del PERCLOS es determinante para la estimación del estado del conductor y que su fusión con otros indicadores de conducción mejora su tasa de aciertos individual. En términos generales, los resultados obtenidos están en concordancia con otros importantes trabajos sobre detección de somnolencia, a excepción de la discusión sobre la importancia de la variable PERCLOS ya que, en esta tesis, se concluye que es el mejor indicador de somnolencia

Detección de fatiga en conductores mediante fusión de sistemas ADAS

Se ha identificado la somnolencia como una de las causas más importante de accidentes de tráfico, ya que se encuentra implicada en el 20% de los mismos, por lo que existe un interés creciente en encontrar sistemas ADAS (Advanced Driver Assistance Systems) capaces de detectar el estado de fatiga del conductor para prevenir posibles accidentes. En esta tesis se propone una técnica, basada en el procesado de imágenes monoculares consistente en la detección, seguimiento y caracterización de la apertura de los ojos, que trabaja automáticamente con distintos usuarios y en condiciones de conducción real. A partir de esta información y de otras señales relativas a la conducción, se infiere la somnolencia del conductor. Para la detección de la cara se ha empleado el algoritmo de detección por apariencia de Viola y Jones, y para la de los ojos se ha mejorado con técnicas de clustering y un filtro de Kalman como predictor. La medida de la apertura de los ojos se ha obtenido aplicando filtros adaptativos, integrales proyectivas y un modelo Gaussiano cuya desviación estándar coincide con la apertura, consiguiendo un sistema en tiempo real y robusto frente a cambios de iluminación. Conocida la apertura se calcula el Porcentaje de Ojo Cerrado (PERCLOS), que es uno de los indicadores más importantes en la detección de somnolencia. Todos resultados han sido obtenidos a partir de una amplia colección de vídeos de la cara de diferentes conductores, en simulación y en condiciones reales, en estado normal y de privación de sueño. Los resultados obtenidos sobre la detección de somnolencia demuestran que la utilización del PERCLOS es determinante para la estimación del estado del conductor y que su fusión con otros indicadores de conducción mejora su tasa de aciertos individual. En términos generales, los resultados obtenidos están en concordancia con otros importantes trabajos sobre detección de somnolencia, a excepción de la discusión sobre la importancia de la variable PERCLOS ya que, en esta tesis, se concluye que es el mejor indicador de somnolencia