Yawn analysis with mouth occlusion detection

tOne of the most common signs of tiredness or fatigue is yawning. Naturally, identification of fatiguedindividuals would be helped if yawning is detected. Existing techniques for yawn detection are centred onmeasuring the mouth opening. This approach, however, may fail if the mouth is occluded by the hand, as itis frequently the case. The work presented in this paper focuses on a technique to detect yawning whilstalso allowing for cases of occlusion. For measuring the mouth opening, a new technique which appliesadaptive colour region is introduced. For detecting yawning whilst the mouth is occluded, local binarypattern (LBP) features are used to also identify facial distortions during yawning. In this research, theStrathclyde Facial Fatigue (SFF) database which contains genuine video footage of fatigued individuals isused for training, testing and evaluation of the system

Video Processing Analysis For Non-Invasive Fatigue Detection And Quantification

Fatigue is a common symptom of weakness either physically or mentally. These symptoms may led to a drop in motivation, weakened sensitivity, slowing of responsiveness and inability to give full attention. All of these problems can cause adverse effects, such as accidents, especially those that require full attention as drivers of vehicles, and rail operators, the pilot of an aircraft or ship operators. This research investigates systems to detect and quantify the signs of fatigue using non-invasive facial analytics. There are four main algorithms that represent the major contribution from the PhD research. These algorithms encompass facial fatigue detection and quantification system as a whole. Firstly, a new technique to detect the face is introduced. This face detection algorithm is an affiliation of colour skin segmentation technique, connected component of binary image usage, and learning machine algorithm. The introduced face detection algorithm is able to reduce the false positive detection rate by a very significant margin. For the facial fatigue detection and quantification, the major fatigue signs features are from the eye activity. A new algorithm called the , Interdependence and Adaptive Scale Mean Shift (IASMS) is presented. The IASMS is able to quantify the state of eye as well as to track non-rigid eye movement. IASMS integrates the mean shift tracking algorithm with an adaptive scale scheme, which is used to track the iris and quantify the iris size. The IASMS is associated with face detection algorithm, image enhanced scheme, eye open detection technique and iris detection method in the initialisation process. This proposed method is able to quantify the eye activities that represent the blink rate and the duration of eye closure. The third contribution is yawning analysis algorithm. Commonly yawning is detected based on a wide mouth opening. Frequently however this approach is thwarted by the common human reaction to hand-cover the mouth during yawning. In this research, a new approach to analyse yawning which takes into account the covered mouth is introduced. This algorithm combines with a new technique of mouth opening measurements, covered mouth detection, and facial distortion (wrinkles) detection. By using this proposed method, yawning is still able to detect even though the mouth is covered. In order to have reliable results from the testing and evaluating of the developed fatigue detection algorithm, the real signs of fatigue are required. This research develops a recorded face activities database of the people that experience fatigue. This fatigue database is called as the Strathclyde Fatigue Facial (SFF). To induce the fatigue signs, ethically approved sleep deprivation experiments were carried out. In these experiments twenty participants, and four sessions were undertaken, which the participant has to deprive their sleep in 0, 3, 5, and 8 hours. The participants were subsequently requested to carry out 5 cognitive tasks that are related to the sleep loss. The last contribution of this research is a technique to recognise the fatigue signs. The existing fatigue detection system is based on single classification. However, this work presents a new approach for fatigue recognition which the fatigue is classified into levels. The levels of fatigue are justified based on the sleep deprivation stages where the SFF database is fully used for training, testing and evaluation of the developed fatigue recognition algorithm. This fatigue recognition algorithm is then integrated into a Fatigue Monitoring Tool (FMT) platform. This FMT has been used to test the participant that carried out the tasks as ship crew in shipping bridge simulator

Co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring system

The increasing number of CCTV cameras in use poses a problem of information overloading for end users. Smart technologies are used in video surveillance to automatically analyze and detect events of interest in real-time, through 2D and 3D video processing techniques called video analytics. This paper presents a smart surveillance stereo vision system for real-time intelligent door access monitoring. The system uses two IP cameras in a stereo configuration and a pan-tilt-zoom (PTZ) camera, to obtain real-time localised, high quality images of any triggering events

Co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring system

The increasing number of CCTV cameras in use poses a problem of information overloading for end users. Smart technologies are used in video surveillance to automatically analyze and detect events of interest in real-time, through 2D and 3D video processing techniques called video analytics. This paper presents a smart surveillance stereo vision system for real-time intelligent door access monitoring. The system uses two IP cameras in a stereo configuration and a pan-tilt-zoom (PTZ) camera, to obtain real-time localised, high quality images of any triggering events

Iris localisation using Fuzzy Centre Detection (FCD) scheme and active contour snake

Iris localisation is a crucial operation in iris recognition algorithm and also in applications, where irises are the main target object. This paper presents a new method to localise iris by using Fuzzy Centre Detection (FCD) scheme and active contour Snake. FCD scheme which consists of four fuzzy membership functions is purposely designed to find a centre of the iris. By using the centre of iris as the reference point, an active contour Snake algorithm is employed to localise the inner and outer of iris boundary. This proposed method is tested and validated with two categories of image database; iris databases and face database. For iris database, UBIRIS.v1, UBIRIS.v2, CASIA.v1, CASIA.v2, MMU1 and MMU2 are used. Whilst for face databases, MUCT, AT&T, Georgia Tech and ZJUblink databases are chosen to evaluate the capability of proposed method to deal with the small size of the iris in the image database. Based on the experimental result, the proposed method shows promising results for both types of databases, including comparison with the some existing iris localisation algorithm

Compression and encryption for ECG biomedical signal in healthcare system

The ECG data needs large memory storage device due to continuous heart rate logs and vital parameter storage. Thus, efficient compression schemes are applied to it before sending it to the telemedicine center for monitoring and analysis. Proper compression mechanisms can not only improve the storage efficiency but also help in faster porting of data from one device to another due to its compact size. Also, the collected ECG signals are processed through various filtering techniques to remove unnecessary noise and then compressed. In our scheme, we propose use of buffer blocks, which is quite novel in this field. Usage of highly efficient methods for peak detection, noise removal, compression and encryption enable seamless and secure transmission of ECG signal from sensor to the monitor. This work further makes use of AES 256 CBC mode, which is barely used in embedded devices, proves to be very strong and efficient in ciphering of the information. The PRD outcome of proposed work comes as 0.41% and CR as 0.35%, which is quite better than existing schemes. Experimental results prove the efficiency of proposed schemes on five distinct signal records from MIT-BIH arrhythmia datasets

Eye Closure and Open Detection Using Adaptive Thresholding Histogram Enhancement (ATHE) Technique and Connected Components Utilisation

Eye closure detection is an important operation prior to carry out the main algorithm such as iris recognition algorithms, and eye tracking algorithms. This paper introduces a method to detect eye closure using Adaptive Thresholding Histogram Enhancement (ATHE) technique and connected component utilisation. The ATHE technique is a combination of histogram enhancement and estimation threshold technique. Firstly, in this proposed method the eye region is required to be localised. The ATHE technique enhances the eye region image then and yield the threshold value to segment the iris region. Based on the segmentation result, the connected components of binary image are used to classify the state of eye whether open or close. This classification is based on the shape and size of segmented region. The performance of the proposed technique is tested and validated by using UBIRIS, MMU and CASIA iris image database

An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset