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The paper presents the comparative analysis of the computer systems for face recognition. Autoencoder, the typical representative of deep learning is compared with the classical PCA transformation. Both, autoencoder and PCA serve as the tools for feature generation and selection. However, the important difference is the nonlinearity and multilayer structure applied in autoencoder. Final task of recognition...
Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in...
This paper describes a linear multi-armed bandit algorithm that exploits sparsity in the underlying unknown weight vector controlling rewards. In linear multi-armed bandits, a user chooses a sequence of (slot machine) “arms” to pull, and each arm pull results in the user receiving a stochastic reward with mean equal to the inner product between a known feature vector associated with the arm and an...
Unknown awareness is very important for many applications such as face recognition. In a typical unknown aware classifier, an “unknown” label is assigned to strange test instances. This study proposes an unknown aware classifier known as UAkNN by extending the well-known kNN classifier. In UAkNN, unknown awareness is achieved by exploiting distances between instances of individual classes. These distances...
This paper compares the performance improvement in recognition rate of different face recognition methods. The face recognition methods such as 1dPCA, 2dPCA, KPCA, ICA and FDA usually use Euclidean distance and in some cases they use the cosine similarity function. Instead of traditional classification and distance measurement methods, SVM classifier is used for classification. The SVM classifier...
Micro-expression recognition is a challenging task in computer vision field due to the repressed facial appearance and short duration. Previous work for micro-expression recognition have used hand-crafted features like LBP-TOP, Gabor filter and optical flow. This paper is the first work to explore the possible use of deep learning for micro-expression recognition task. Due to the lack of data for...
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images...
In this work, we investigate the problem of predicting gender from still images using human metrology. Since the values of the anthropometric measurements are difficult to be estimated accurately from state-of-the-art computer vision algorithms, ratios of anthropometric measurements were used as features. Additionally, since several measurements will not be available at test time in a real-life scenario,...
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