scholarly journals A Biometric Recognition Method Using Deep CNN

2021 ◽  
Vol 9 (11) ◽  
pp. 1939-1943
Author(s):  
Vishalakshi Rituraj
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Transfer Learning ◽  
Recognition System ◽  
Biometric Recognition ◽  
Recognition Method ◽  
Special Training ◽  
Complex Calculation ◽  
Deep Cnn ◽  
Biometric Trait

Abstract: Face is perhaps the first biometric trait of a person that catches one’s eye and it remains in memory for a long due to its uniqueness created by almighty. Recognizing a person using his/her face, is very natural to us and we do not need any special training for identification. But computers are programmed for analyzing things and making predictions almost in similar fashion that our brain does. Then, the recognition takes place by using some techniques and trainings. The recognition system which uses biometric properties is itself a secure and trusted technique but use of neural networks make it highly accurate and add more worth to it. A CNN model works in a fully supervised or guided environment and performs all the tasks in a robotic manner. The convolutional layer which lies in CNN model performs the complex calculation and extracts all the unique and useful features without any human involvement. I preferred to adopt Transfer learning in my work, by importing a pre-trained CNN model and I found 97.5% accuracy in recognition when I tested the model with my test samples. Keywords: Biometrics, Convolution, AlexNet, Feature Extraction, Transfer Learning

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing

2020 ◽  
Vol 10 (17) ◽  
pp. 5792 ◽  
Author(s):  
Biserka Petrovska ◽  
Tatjana Atanasova-Pacemska ◽  
Roberto Corizzo ◽  
Paolo Mignone ◽  
Petre Lameski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Transfer Learning ◽  
Remote Sensing Image ◽  
Learning Rate ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Rates

Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods.

scholarly journals RESEARCH AND DEVELOPMENT OF TEXT RECOGNITION SYSTEM

2020 ◽  
Author(s):  
Oleksii Denysenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
High Reliability ◽  
Recognition System ◽  
Text Recognition ◽  
Light Conditions ◽  
Recognition Method ◽  
Low Light ◽  
Series Of Experiments

The paper discusses the technology of creating character recognition (using convolutional neural networks) systems on the image. These days, there are many approaches to solving this problem, and most of them are ineffective for images whose symbols are located on a complex background and are vulnerable to noise, affine and projection distortions. The proposed technique consists of the following stages: image pre-processing, text segmentation, and recognition by convolutional neural networks. During research was conducted a series of experiments, namely: experiment to select the most suitable method of binarization of digital images, experiment to select the most efficient convolutional neural network topology form text recognition problem. As a result of the experiments performed, this technique as applied to the recognition of car numbers demonstrates high reliability and accuracy, including in low light conditions, therefore, the developed recognition method can be recommended for commercial use. As an additional field of experiments was suggested a bunch of approaches of how to improve this technique.

scholarly journals A fast Iris recognition system through optimum feature extraction

2018 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals Iqro Reading Learning System through Speech Recognition Using Mel Frequency Cepstral Coefficient (MFCC) and Vector Quantization (VQ) Method

2018 ◽  
Vol 2 (01) ◽  
pp. 29
Author(s):  
Youllia Indrawaty Nurhasanah ◽  
Irma Amelia Dewi ◽  
Bagus Ade Saputro
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Vector Quantization ◽  
Recognition System ◽  
Learning System ◽  
Recognition Method ◽  
Signal Features ◽  
Extraction Step ◽  
Speech Feature ◽  
Mel Frequency Cepstral Coefficient

Historically, the study of Qur'an in Indonesia evolved along with the spread of Islam. Learning methods of reading the Qur'an have been found ranging from al-Baghdadi, al-Barqi, Qiraati, Iqro', Human, Tartila, and others, which can make it easier to learn to read the Qur'an. Currently, the development of speech recognition technology can be used for the detection of Iqro vol 3 reading pronunciations. Speech recognition consists of two general stages of feature extraction and speech matching. The feature extraction step is used to derive speech-feature and speech-matching stages to compare compatibility between test sound and train voice. The speech recognition method used to recognize Iqro readings is extracting speech signal features using Mel Frequency Cepstral Coefficient (MFCC) and classifying them using Vector Quantization (VQ) to get the appropriate speech results. The result of testing for speech recognition system of Iqro reading has been tested for 30 peoples as a sample of data and there are 6 utterances indicating the information failed, so the system has a success rate of 80%.

CNN-based multimodal touchless biometric recognition system using Gait and speech

2021 ◽  
pp. 1-10
Author(s):  
Sumit Sarin ◽  
Antriksh Mittal ◽  
Anirudh Chugh ◽  
Smriti Srivastava
Keyword(s):  
Neural Networks ◽  
Weighted Average ◽  
Recognition System ◽  
Biometric Recognition ◽  
Person Identification ◽  
Learning Techniques ◽  
Biometric Systems ◽  
Multimodal Biometric Systems ◽  
Biometric Features ◽  
Identification Model

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

scholarly journals Deep Learning Based Image Recognition for 5G Smart IoT Applications

2020 ◽  
Author(s):  
Jing Li ◽  
Xinfang li ◽  
Yuwen Ning
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Recognition Method ◽  
Linear Discriminant

Abstract With the advent of the 5G era，the development of massive data learning algorithms and in-depth research on neural networks, deep learning methods are widely used in image recognition tasks. However, there is currently a lack of methods for identifying and classifying efficiently Internet of Things (IoT) images. This paper develops an IoT image recognition system based on deep learning, i.e., uses convolutional neural networks (CNN) to construct image recognition algorithms, and uses principal component analysis (PCA) and linear discriminant analysis (LDA) to extract image features, respectively. The effectiveness of the two PCA and LDA image recognition methods is verified through experiments. And when the image feature dimension is 25, the best image recognition effect can be obtained. The main classifier used for image recognition in the IoT is the support vector machine (SVM), and the SVM and CNN are trained by using the database of this paper. At the same time, the effectiveness of the two for image recognition is checked, and then the trained classifier is used for image recognition. It is found that a CNN and SVM-based secondary classification IoT image recognition method improves the accuracy of image recognition. The secondary classification method combines the characteristics of the SVM and CNN image recognition methods, and the accuracy of the image recognition method is verified to provide an effective improvement through experimental verification.

Sign in / Sign up

Export Citation Format

Share Document