Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning approach from Persian handwriting domain to multi-language OSV domain. We train two Residual CNNs on the source domain separately based on two different tasks of word classification and writer identification. Since identifying a person signature resembles identifying ones handwriting, it seems perfectly convenient to use handwriting for the feature learning phase. The learned representation on the more varied and plentiful handwriting dataset can compensate for the lack of training data in the original task, i.e. OSV, without sacrificing the generalizability. Our proposed OSV system includes two steps: learning representation and verification of the input signature. For the first step, the signature images are fed into the trained Residual CNNs. The output representations are then used to train SVMs for the verification. We test our OSV system on three different signature datasets, including MCYT (a Spanish signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 17.45%. Our proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving 6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best previously reported results

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

<p>Abstract</p> <p>Background</p> <p>It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of <it>Saccharomyces Cerevisiae</it>'s proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.</p> <p>Results</p> <p>We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.</p> <p>Conclusions</p> <p>We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of <it>Saccharomyces Cerevisiae'</it>s proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.</p

A novel approach to automatic music composing: Using genetic algorithm

Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering entropy of the notes distribution as a factor of fitness function and developing mutation and crossover functions based on harmonic rules and trying to keep the melodies intact during these processes would result in a musical piece pleasant to human ears and interesting for human mind. This algorithm does not have the constraints of the previous algorithms. Restraining mutation and crossover functions with a goal of producing melodies based on acceptable melodies composed by humans, this algorithm is not bound to any genre, instrument or melody. The experimental results of this approach show that it is near to the human composing and the results produced from it are more acceptable than the ones produced by its predecessors

Active Transfer Learning for Persian Offline Signature Verification

Offline Signature Verification (OSV) remains a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during the training. This challenge is aggravated when there are small labeled training data available but with large intra-personal variations. In this study, we address this issue by employing an active learning approach, which selects the most informative instances to label and therefore reduces the human labeling effort significantly. Our proposed OSV includes three steps: feature learning, active learning, and final verification. We benefit from transfer learning using a pre-trained CNN for feature learning. We also propose SVM-based active learning for each user to separate his genuine signatures from the random forgeries. We finally used the SVMs to verify the authenticity of the questioned signature. We examined our proposed active transfer learning method on UTSig: A Persian offline signature dataset. We achieved near 13% improvement compared to the random selection of instances. Our results also showed 1% improvement over the state-of-the-art method in which a fully supervised setting with five more labeled instances per user was used

A SVM-based method for face recognition using a wavelet PCA representation

ABSTRACT This paper proposes a new method of fac

Online learning of taskdriven object-based visual attention control

A biologically-motivated computational model for learning task-driven and objectbased visual attention control in interactive environments is proposed. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Online Learning of Task-driven Object-based Visual Attention Control Ali Borji Top-down attention is learned by the U-TREE Discussions and Conclusions An agent working in an environment receives information momentarily through its visual sensor. It should determine what to look for. For this we use RL to teach the agent simply look for the most task relevant and rewarding entity in the visual scene ( This layer controls both top-down visual attention and motor actions. The learning approach is an extension of the U-TREE algorithm [6] to the visual domain. Attention tree is incrementally built in a quasi-static manner in two phases (iterations): 1) RL-fixed phase and 2) Tree-fixed phase In each Tree-fixed phase, RL algorithm is executed for some episodes by Fig. 1. Proposed model for learning task-driven object-based visual attention control Example scenario: captured scene through the agents&apos; visual sensor undergoes a biased bottom-up saliency detection operation and focus of attention (FOA) is determined. Object at the FOA is recognized (i.e. is either present or not in the scene), then the agent moves in its binary tree in the decision making and leaves. 100% correct policy was achieved. The object at the attended location is recognized by the hierarchical model of object recognition (HMAX) [3] M. Riesenhuber, T. Poggio, Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(1999),11, 1019-1025. Basic saliency-based model of visual attention [1] is revised for the purpose of salient region selection (object detection) at this layer where norm(.) is the Euclidean distance between two points in an image. Saliency is the function which takes as input an image and a weight vector and returns the most salient location. t i is the location of target object in the i-th image. In each Tree-fixed phase, RL algorithm is executed for some episodes by following ε-greedy action selection strategy. In this phase, tree is hold fixed and the derived quadruples (s t , a t , r t+1 , s t+1 ) are only used for updating the Q-table: State discretization occurs in the RL-fixed phase where gathered experiences are used to refine aliased states. An object which minimizes aliasing the most is selected for braking an aliased leaf. Acknowledgement This work was funded by the school of cognitive sciences, IPM, Tehran, IRAN. scene), then the agent moves in its binary tree in the decision making and learning layer. This is done repetitively until it reaches a leaf node which determines its state. The best motor action is this state is performed. Outcome of this action over the world is evaluated by a critic and a reinforcement signal is fed back to the agent to update its internal representations (attention tree) and action selection strategy in a quasi-static manner. Following subsections discuss each layer of the model in detail