Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201

Group-level Emotion Recognition using Transfer Learning from Face Identification

In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand Challenge

Incorporación de atributos faciales a sistemas de reconocimiento facial

Máster Universitario en Investigación e Innovación en Tecnologías de la Información y las ComunicacionesLa aparición de redes neuronales profundas ha provocado un gran progreso en el ámbito de la biometría. Los sistemas de reconocimiento facial son cada vez más utilizados y cada vez requieren una mayor precisión. Un modo habitual de mejorar estos sistemas es el refuerzo mediante atributos característicos de cada persona, los llamados soft biometrics. El género, la edad o la raza son algunos de los atributos más habituales. Al analizar el rendimiento de los sistemas de reconocimiento facial se observan diferencias dentro de cada grupo demográ co. Atendiendo al género, las mujeres son las que peores resultados obtienen. Para el caso de la raza, son las personas de raza negra o asiática las que normalmente presentan más di cultades en el reconocimiento facial. Este problema radica principalmente en los conjuntos de entrenamiento con los que los modelos han aprendido. Estos no suelen estar balanceados y se re eja en los resultados cuando analizamos cada clase. Normalmente las bases de datos incluyen más hombres y más identidades de raza blanca. En este trabajo se desarrollan sistemas especí cos para los grupos demográ cos de género y raza. Los resultados experimentales demuestran que utilizando modelos entrenados con imágenes pertenecientes a una única clase se mejora el rendimiento de un sistema de reconocimiento facial genérico que ha sido entrenado con imágenes de todas las clases. Se proponen también dos estimadores para los atributos de género y raza. Se compara el rendimiento del sistema cuando la información de dichos atributos es obtenida de manera manual, es decir mediante etiquetas y cuando se extrae de manera automática. Además se propone un sistema más completo que fusiona la información de género y raza. Y se analizan las alternativas de fusión a nivel de features y a nivel de scores.The research in deep neural networks has produced a great improvement in the world of biometrics. Facial recognition systems are used more often and require a higher accuracy. A common way of improving these systems is the reinforcement through characteristic attributes from each person which are known as soft biometrics. The gender, age or ethnic group are the most common attributes. Analyzing the performance of facial recognition systems, di erences are observed within each demographic group. Considering the gender, women obtain the worst results. Regarding the ethnicity group, dark skin persons or asian have more di culties in the facial recognition. This problem is mainly due to the training sets used for the learning process of the models. These are not usually balanced and that is re ected in the results obtained for each class. Usually datasets include more men and more white race identities. In this project, speci c models are developed for the demographic groups of gender and ethnicity. The experimental results show that using trained models with images from a single class, it is possible to improve the performance of a generic facial recognition system trained with images from all classes. Two estimators for the gender and ethnic group attributes are also proposed. System performance is compared when race and gender information is obtained automatically or manually, through label. Moreover, a more complete system is proposed combining gender and ethnic group information. Proposing a fusion of this information at the scores or the features level