Feature Learning Approach for Facial Recognition Using Deep Metric Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 4125-4130
Author(s):  
Gaurav Karkal ◽  
K. Dhanush Reddy ◽  
Kaushik Singh ◽  
Nikith Hosangadi ◽  
Annapurna P. Patil
Keyword(s):  
Neural Network ◽  
Facial Recognition ◽  
Metric Learning ◽  
Feature Learning ◽  
Human Interaction ◽  
Single Image ◽  
Whole Process ◽  
Knn Classifier ◽  
Deep Metric Learning ◽  
Trained Neural Network

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

scholarly journals Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1593
Author(s):  
Hongying Liu ◽  
Ruyi Luo ◽  
Fanhua Shang ◽  
Xuechun Meng ◽  
Shuiping Gou ◽  
...  
Keyword(s):  
Real World ◽  
Metric Learning ◽  
Feature Learning ◽  
Linear Mapping ◽  
Layer By Layer ◽  
Learning Networks ◽  
Distance Metric ◽  
Linear Projection ◽  
Deep Metric Learning

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

scholarly journals Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

2021 ◽  
Vol 11 (15) ◽  
pp. 6959
Author(s):  
Zaky Dzulfikri ◽  
Pin-Wei Su ◽  
Chih-Yung Huang
Keyword(s):  
Neural Network ◽  
Metric Learning ◽  
Training Data ◽  
Probability Method ◽  
Learning Approach ◽  
Test Results ◽  
Complex Signals ◽  
Intelligent Monitoring ◽  
Deep Metric Learning

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

scholarly journals Predicting TCR-epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

2021 ◽  
Author(s):  
Alan Luu ◽  
Jacob R Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of CDR3B sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR important for binding specificity. Contrary to the common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides insight into the learned predictive features of TCR-epitope binding specificity and advances associated classification tasks.

scholarly journals Rare bioparticle detection via deep metric learning

RSC Advances ◽  
2021 ◽  
Vol 11 (29) ◽  
pp. 17603-17610
Author(s):  
Shaobo Luo ◽  
Yuzhi Shi ◽  
Lip Ket Chin ◽  
Yi Zhang ◽  
Bihan Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Networks ◽  
Metric Learning ◽  
Practical Applications ◽  
Deep Metric Learning

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.

scholarly journals Deep Metric Learning with Online Hard Mining for Hyperspectral Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1368
Author(s):  
Yanni Dong ◽  
Cong Yang ◽  
Yuxiang Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Metric Learning ◽  
Hyperspectral Data ◽  
Mining Method ◽  
Raw Data ◽  
The Core ◽  
Embedded Network ◽  
Deep Metric Learning ◽  
Hyperspectral Classification

Recently, deep learning has developed rapidly, while it has also been quite successfully applied in the field of hyperspectral classification. Generally, training the parameters of a deep neural network to the best is the core step of a deep learning-based method, which usually requires a large number of labeled samples. However, in remote sensing analysis tasks, we only have limited labeled data because of the high cost of their collection. Therefore, in this paper, we propose a deep metric learning with online hard mining (DMLOHM) method for hyperspectral classification, which can maximize the inter-class distance and minimize the intra-class distance, utilizing a convolutional neural network (CNN) as an embedded network. First of all, we utilized the triplet network to learn better representations of raw data so that raw data were capable of having their dimensionality reduced. Afterward, an online hard mining method was used to mine the most valuable information from the limited hyperspectral data. To verify the performance of the proposed DMLOHM, we utilized three well-known hyperspectral datasets: Salinas Scene, Pavia University, and HyRANK for verification. Compared with CNN and DMLTN, the experimental results showed that the proposed method improved the classification accuracy from 0.13% to 4.03% with 85 labeled samples per class.

Dysarthric Speech Recognition Based on Deep Metric Learning

2020 ◽  
Author(s):  
Yuki Takashima ◽  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Speech Recognition ◽  
Metric Learning ◽  
Deep Metric Learning ◽  
Dysarthric Speech
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