Mohammad Hajizadeh Saffar

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Signature verification techniques employ various specifications of a signature. Feature extraction and feature selection have an enormous effect on accuracy of signature verification. Feature extraction is a difficult phase of signature verification systems due to different shapes of signatures and different situations of sampling. This paper presents a(More)
This paper presents a novel method to involve both spatial and temporal features for semantic segmentation of street scenes. Current work on convolutional neural networks (CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for the semantic segmentation task. We investigate how involving temporal(More)
—Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this(More)
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders. We model the represented users' finger vein(More)
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