Face recognition in low-resolution images under small sample conditions with face-part detection and alignment

Om menselijke gebruikers te ondersteunen bij dagelijkse taken, moeten computers op de hoogte zijn van de aanwezigheid en identiteit van mensen. Tegenwoordig is het heel makkelijk om beeldinformatie te verkrijgen door middel van bijvoorbeeld smart phones. Daarom worden KI-gebaseerde algoritmen voor gezichtsdetectie en gezichtsherkenning steeds belangrijker. Gezichtsherkenning is een zeer eenvoudige en handige techniek vergeleken met andere biometrische methoden zoals vingerafdruk- of irisherkenning. Echter, deze techniek is niet erg robuust vergeleken met menselijke prestaties en minder betrouwbaar dan alternatieve biometrische methoden. Dit komt doordat er veel variaties in foto's en video voorkomen (bijvoorbeeld verlichting, pose), die uitdagende problemen voor een gezichtsherkenningsalgoritme creëren. In dit proefschrift hebben we ons vooral gericht op drie van deze uitdagingen. De eerste twee zijn de lokalisatie en de uitlijning in draairichting van gezichten, die preprocessing stappen zijn voordat de herkenning plaatsvindt. De derde stap is gezichtsidentificatie zelf, op basis van zeer weinig trainingsdata. Voor de lokalisatie ontwikkelden we een oogdetector, die oogcentra lokaliseert op basis van een gedetecteerd gezicht. Rotatie-uitlijning wordt gedaan met behulp van de hoeken van deze oogcentra. We stellen twee nieuwe methoden voor om om te gaan met weinig leervoorbeelden. De experimenten leiden tot twee belangrijke conclusies: ten eerste kan een grote generieke dataset helpen om de prestaties van herkenning aanzienlijk te verbeteren voor nieuwe gezichten. Ten tweede, als het aantal foto's beperkt is, dan helpt het gebruik van vele patches om de identificatienauwkeurigheid te vergoten. Samenvattend, hoewel ons onderzoek bijdraagt aan het oplossen van gezichtsidentificatie wanneer slechts een kleine dataset beschikbaar is, is verder onderzoek noodzakelijk om robuustere resultaten te verkrijgen

Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been demonstrated in the closely related task of face verification using deep convolutional neural networks. In this paper we explore the applicability of deep convolutional neural networks on gender classification by fine-tuning a pretrained neural network. In addition, we explore the performance of dropout support vector machines by training them on the deep features of the pretrained network as well as on the deep features of the fine-tuned network. We evaluate our methods on the color FERET data collection and the recently constructed Adience data collection. We report crossvalidated performance rates on each dataset. We further explore generalization capabilities of our approach by conducting crossdataset tests. It is demonstrated that our fine-tuning method exhibits state-of-the-art performance on both datasets

Machine learning for multi-view eye-pair detection

While face and eye detection is well known research topics in the field of object detection, eye-pair detection has not been much researched. Finding the location and size of an eye-pair in an image containing a face can enable a face recognition application to extract features from a face corresponding to different entities. Furthermore, it allows us to align different faces, so that more accurate recognition results can be obtained. To the best of our knowledge, currently there is only one eye-pair detector, which is a part of the Viola-Jones object detection framework. However, as we will show in this paper, this eye-pair detector is not very accurate for detecting eye-pairs from different face images. Therefore, in this paper we describe several novel eye-pair detection methods based on different feature extraction methods and a support vector machine (SVM) to classify image patches as containing an eye-pair or not. To find the location of an eye-pair on unseen test images, a sliding window approach is used, and the location and size of the window giving the highest output of the SVM classifier are returned. We have tested the different methods on three different datasets: the IMM, the Caltech and the Indian face dataset. The results show that the linear restricted Boltzmann machine feature extraction technique and principal component analysis result in the best performances. The SVM with these feature extraction methods is able to very accurately detect eye-pairs. Furthermore, the results show that our best eye-pair detection methods perform much better than the Viola-Jones eye-pair detector. (C) 2014 Elsevier Ltd. All rights reserved

Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been demonstrated in the closely related task of face verification using deep convolutional neural networks. In this paper we explore the applicability of deep convolutional neural networks on gender classification by fine-tuning a pretrained neural network. In addition, we explore the performance of dropout support vector machines by training them on the deep features of the pretrained network as well as on the deep features of the fine-tuned network. We evaluate our methods on the color FERET data collection and the recently constructed Adience data collection. We report crossvalidated performance rates on each dataset. We further explore generalization capabilities of our approach by conducting crossdataset tests. It is demonstrated that our fine-tuning method exhibits state-of-the-art performance on both datasets

Robust Face Recognition by Computing Distances from Multiple Histograms of Oriented Gradients

The Single Sample per Person Problem is a challenging problem for face recognition algorithms. Patch-based methods have obtained some promising results for this problem. In this paper, we propose a new face recognition algorithm that is based on a combination of different histograms of oriented gradients (HOG) which we call Multi-HOG. Each member of Multi-HOG is a HOG patch that belongs to a grid structure. To recognize faces, we create a vector of distances computed by comparing train and test face images. After this, a distance calculation method is employed to calculate the final distance value between a test and a reference image. We describe here two distance calculation methods: mean of minimum distances (MMD) and a multi-layer perceptron based distance (MLPD) method. To cope with aligning difficulties, we also propose another technique that finds the most similar regions for two compared images. We call it the most similar region selection algorithm (MSRS). The regions found by MSRS are given to the algorithms we proposed. Our results show that, while MMD and MLPD contribute to obtaining much higher accuracies than the use of a single histogram of oriented gradients, combining them with the most similar region selection algorithm results in state-of-the-art performances