Likelihood Ratio-Based Detection of Facial Features

One of the first steps in face recognition, after image acquisition, is registration. A simple but effective technique of registration is to align facial features, such as eyes, nose and mouth, as well as possible to a standard face. This requires an accurate automatic estimate of the locations of those features. This contribution proposes a method for estimating the locations of facial features based on likelihood ratio-based detection. A post-processing step that evaluates the topology of the facial features is added to reduce the number of false detections. Although the individual detectors only have a reasonable performance (equal error rates range from 3.3% for the eyes to 1.0% for the nose), the positions of the facial features are estimated correctly in 95% of the face images

Detection of Facial Features in Scale-Space

This paper presents a new approach to the detection of facial features. A scale adapted Harris Corner detector is used to find interest points in scale-space. These points are described by the SIFT descriptor. Thus invariance with respect to image scale, rotation and illumination is obtained. Applying a Karhunen-Loeve transform reduces the dimensionality of the feature space. In the training process these features are clustered by the k-means algorithm, followed by a cluster analysis to find the most distinctive clusters, which represent facial features in feature space. Finally, a classifier based on the nearest neighbor approach is used to decide whether the features obtained from the interest points are facial features or not.

Detection of Facial Features From 3D Face Model Acquired by the Kinect Sensor

Obsahem této bakalářské práce je zkoumání a navrhnutí metody pro detekci rysů tváře (nosu, očí a ůst). Detekce probíha na 3D modelech získaných pomocí přístroje Kinect. Kromě návrhu a implementace aplikace jsou v dokumentě zahrnuty i dosažné výsledky experimentů detekce na různych vzorcích a jejich vyhodnocení.The subject of this bachelor thesis is study and design of facial features detection (nose, eyes and mouth). The detection is applied on 3D models acquired by Kinect device. Besides the design and implementation of application, this document also includes experimenting with the application on the set of various models and evaluation of the results.

Prototype Drowsiness Detection System

Driver fatigue is one of the major causes of accidents in the world. Detecting the drowsiness of the driver is one of the surest ways of measuring driver fatigue. In this project we aim to develop a prototype drowsiness detection system. This system works by monitoring the eyes of the driver and sounding an alarm when he/she is drowsy. The system so designed is a non-intrusive real-time monitoring system. The priority is on improving the safety of the driver without being obtrusive. In this project the eye blink of the driver is detected. If the drivers eyes remain closed for more than a certain period of time, the driver is said to be drowsy and an alarm is sounded. The programming for this is done in OpenCV using the Haarcascade library for the detection of facial features

Drowsy Driver Detection System

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we aim to develop an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system so constructed is a non-intrusive real-time observing system. The primacy is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver’s eyes remain closed for more than a certain span of time, the driver is believed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Drowsy Driver Detection System (DDDS)

Driver weariness is one of the key causes of road mishaps in the world. Detecting the drowsiness of the driver can be one of the surest ways of quantifying driver fatigue. In this project we have developed an archetype drowsiness detection system. This mechanism works by monitoring the eyes of the driver and sounding an alarm when he/she feels heavy eyed. The system constructed is a non-intrusive real-time perceiving system. The priority is on improving the safety of the driver. In this mechanism the eye blink of the driver is detected. If the driver?s eyes remain closed for greater than a certain period of time, the driver is deemed to be tired and an alarm is sounded. The programming for this is carried out in OpenCV using the Haar cascade library for the detection of facial features

Comparing landmarking methods for face recognition

Good registration (alignment to a reference) is essential for accurate face recognition. We use the locations of facial features (eyes, nose, mouth, etc) as landmarks for registration. Two landmarking methods are explored and compared: (1) the Most Likely-Landmark Locator (MLLL), based on maximizing the likelihood ratio [1], and (2) Viola-Jones detection [2]. Further, a landmark-correction method based on projection into a subspace is introduced. Both landmarking methods have been trained on the landmarked images in the BioID database [3]. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5 landmarks. The localization error and effects on the equal-error rate (EER) have been measured. In these experiments ground- truth data has been used as a reference. The results are described as follows:\ud 1. The localization errors obtained on the FRGC database are 4.2, 8.6 and 4.6 pixels for the Viola-Jones, the MLLL, and the MLLL after landmark correction, respectively. The inter-eye distance of the reference face is 100 pixels. The MLLL with landmark correction scores best in the verification experiment.\ud 2. Using more landmarks decreases the average localization error and the EER

Pengamatan Ekspresi Wajah Secara Adaptif Dengan Presentasi Pemutaran Musik

High life demands nowadays make the individual emotional state change easily. Music is one of many media that is used to stabilize a person's emotional condition. However, wrong use of music can worsen a person's emotional condition, as in the case of today's society.To prevent that, an application that can detect the user's facial expression to stabilize the user's emotional state through music is developed. The expression on the face is determined by the position of eyebrows, eyes, mouth, and wrinkles features. The positions of detected features are compared with those in a neutral face that has been previously calibrated. The difference of features' positions will be used as input of the trained neural network to determine the expression on the detected face.Through some experiments, the accuracy of detection of facial features and facial expressions are known. The detected expression will trigger the playback of music to stabilize the user's emotional condition. The weakness of this application is that the music library must be set manually by the user