Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

<!-- *** Custom HTML *** --> With recent advances in pattern recognition and computer vision, mobile palmprint authentication has become an emerging field to provide better facilities and ubiquitous computing for scientific and commercial communities. To effectively streamline this issue, researchers focus on improving authentication performance by designing deep convolutional neural networks. Despite the high potential of the state-of-the-art methods, the challenges of preprocessing computation cost, lack of training samples for big data application, and discriminative feature optimization remain to be carefully addressed. A deep mobile palmprint verification framework focusing on discriminative feature representation is proposed. To this end, an automatic feature mapping is learned from two well-known deep architectures via an effective weighted loss function. Thereafter, a convolution-based feature fusion block is followed by a surrogate model in the feature-matching phase for palmprint verification. From a practical point of view, our framework is cost-effective and can represent discriminative features with high performance. We demonstrate the effectiveness of our framework and mobile database for palmprint verification task beating the state-of-the-art on standard benchmarks. Moreover, experimental results show that our model outperforms previous ones, especially for the few-shot learning application, achieving equal error rates of 0.0281% and 0.0197% for IIT Delhi Touchless Palmprint Database and Hong Kong PolyU Palmprint databases, respectively. It is notable that all codes are open-source and may be accessed online.

In this article, we calculated and modeled the gain of In_0.53Ga_0.47As/InP avalanche photodiode (APD) based on a device mechanism and carrier rate equations using transfer matrix method (TMM). In fact, a distributed model was presented for calculating impact ionization (I²) and relating different sections of the multiplication region. In this proposed model, recessive equations were used, and device gain is considered proportional to the number of output photo-electrons and photo-holes. By comparison of simulated results with experimental data available in literature, it has been demonstrated the capability of the developed model as a powerful tool for simulating APDs’ behavior and interpreting their experimentally measured characteristics.

In this paper, In0.53Ga0.47As/InP avalanche photodiode (APD) gain is calculated based on the device mechanism and carrier rate equations, using transfer matrix method (TMM). In fact, a distributed model is presented for calculating impact ionization and relates different sections of the multiplication region. In proposed model, recessive equations are used and device gain is considered proportional to the number of output photo-electrons and photo-holes. Comparison of simulation results with experimental data available in literature has demonstrated the capability of the new developed model as a powerful tool to simulate the APDs' behavior and to interpret their experimentally measured characteristics.