Computing driver tiredness and fatigue in automobile via eye tracking and body movements

The aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as 'Alert' or 'Drowsy' for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing

Deep learning architectures for Computer Vision

Deep learning has become part of many state-of-the-art systems in multiple disciplines (specially in computer vision and speech processing). In this thesis Convolutional Neural Networks are used to solve the problem of recognizing people in images, both for verification and identification. Two different architectures, AlexNet and VGG19, both winners of the ILSVRC, have been fine-tuned and tested with four datasets: Labeled Faces in the Wild, FaceScrub, YouTubeFaces and Google UPC, a dataset generated at the UPC. Finally, with the features extracted from these fine-tuned networks, some verifications algorithms have been tested including Support Vector Machines, Joint Bayesian and Advanced Joint Bayesian formulation. The results of this work show that an Area Under the Receiver Operating Characteristic curve of 99.6% can be obtained, close to the state-of-the-art performance.El aprendizaje profundo se ha convertido en parte de muchos sistemas en el estado del arte de múltiples ámbitos (especialmente en visión por computador y procesamiento de voz). En esta tesis se utilizan las Redes Neuronales Convolucionales para resolver el problema de reconocer a personas en imágenes, tanto para verificación como para identificación. Dos arquitecturas diferentes, AlexNet y VGG19, ambas ganadores del ILSVRC, han sido afinadas y probadas con cuatro conjuntos de datos: Labeled Faces in the Wild, FaceScrub, YouTubeFaces y Google UPC, un conjunto generado en la UPC. Finalmente con las características extraídas de las redes afinadas, se han probado diferentes algoritmos de verificación, incluyendo Maquinas de Soporte Vectorial, Joint Bayesian y Advanced Joint Bayesian. Los resultados de este trabajo muestran que el Área Bajo la Curva de la Característica Operativa del Receptor puede llegar a ser del 99.6%, cercana al valor del estado del arte.L’aprenentatge profund s’ha convertit en una part importat de molts sistemes a l’estat de l’art de múltiples àmbits (especialment de la visió per computador i el processament de veu). A aquesta tesi s’utilitzen les Xarxes Neuronals Convolucionals per a resoldre el problema de reconèixer persones a imatges, tant per verificació com per identificatió. Dos arquitectures diferents, AlexNet i VGG19, les dues guanyadores del ILSVRC, han sigut afinades i provades amb quatre bases de dades: Labeled Faces in the Wild, FaceScrub, YouTubeFaces i Google UPC, un conjunt generat a la UPC. Finalment, amb les característiques extretes de les xarxes afinades, s’han provat diferents algoritmes de verificació, incloent Màquines de Suport Vectorial, Joint Bayesian i Advanced Joint Bayesian. Els resultats d’aquest treball mostres que un Àrea Baix la Curva de la Característica Operativa del Receptor por arribar a ser del 99.6%, propera al valor de l’estat de l’art

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database