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Ear Recognition using Chainlet based Multi-Band SVM
2021
2021 IEEE International Conference on Imaging Systems and Techniques (IST)
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Experimental results on images of two benchmark ear image datasets show that the proposed CMBER-SVM technique outperforms both the state of the art statistical and learning based ear recognition methods ...
This paper presents a Chainlet based Multi-Band Ear Recognition using Support Vector Machine (CMBER-SVM) algorithm. ...
ACKNOWLEDGMENT Matthew Martin Zarachoff thanks Leeds Beckett University for their support through a fully-funded studentship. ...
doi:10.1109/ist50367.2021.9651413
fatcat:lglvyspexzhcni6vpumymf35lq
Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
2022
Applied Sciences
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Furthermore, the proposed CERMB-SVM method yields greater performance in comparison to its anchor chainlet technique and state-of-the-art learning-based ear recognition techniques. ...
This paper introduces the Chainlet-based Ear Recognition algorithm using Multi-Banding and Support Vector Machine (CERMB-SVM). ...
Some of these methods include: 'Eigenfaces' [6] , wavelet [8] , SVM [14, 15] and deep learning [12] based techniques for feature extraction and classification. ...
doi:10.3390/app12042033
fatcat:ihsaknbhmfcx5mcuuq7g2gzopa
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
2018
Information
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More recently, most ear recognition methods have started based on deep learning features that can achieve a good accuracy, but it requires more resources in the training phase and suffer from time-consuming ...
Therefore, in this paper, we adopt the descriptor features and present a novel metric learning method that is efficient in matching real-time for ear recognition system. ...
[27] presented a personal neural network for ear recognition and explained a brief review for deep learning ear recognition. On the other hand, Omara et al. ...
doi:10.3390/info9090215
fatcat:7v4wllmz6ffslfrtva74yenzly
A Hybrid Approach Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric System
2020
IEEE Access
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Therefore, it is interesting and very attractive to propose a novel framework for multimodal biometric recognition based on Learning Distance Metric (LDM) via kernel SVM. ...
This paper considers metric learning for SVM by investigating a hybrid Learning Distance Metric and Directed Acyclic Graph SVM (LDM-DAGSVM) model for multimodal biometric recognition, where LDM and DAGSVM ...
Moreover, LDM via kernel SVM is investigated for ear classification based on an RBF-SVM classifier. ...
doi:10.1109/access.2020.3035110
fatcat:rolxfbcfibfmhm5qdizvjccxiu
When Old Meets New: Emotion Recognition from Speech Signals
2021
Cognitive Computation
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Moreover, the success of the MFCC-LSTM model is evidence that, despite being conventional features, MFCCs can still outperform more sophisticated deep-learning feature sets. ...
Recent works in SER have been focused on end-to-end deep neural networks (DNNs). ...
In particular, deep-learned features combined with SVM have been shown to achieve state-of-the-art performance [44] . ...
doi:10.1007/s12559-021-09865-2
fatcat:ybhaoumutvbwpjqzpeas3ymroi
Recent Iris and Ocular Recognition Methods in High- and Low-Resolution Images: A Survey
2022
Mathematics
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Furthermore, since existing survey papers have focused on and summarized studies on traditional handcrafted feature-based iris and ocular recognition, this survey paper also introduced the latest deep ...
learning-based methods in detail. ...
geometric, local methods; deep neural networks based on the features of ear region [11] ; and iris recognition which segments and uses only the iris [12] . ...
doi:10.3390/math10122063
doaj:bc3c679922eb4abc89053436ea5ea2c6
fatcat:wovejhs4vzd25irl2wo2yi7ula
Noisy Iris Recognition Based on Deep Neural Network
2020
Menoufia Journal of Electronic Engineering Research
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Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively. ...
Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. ...
Convolutional neural network (CNN) is one of deep learning methods, which designed for purpose of image and video processing. ...
doi:10.21608/mjeer.2020.103276
fatcat:zifmyobppvhgpejwinvirbjjrm
Application of Single Image Super-Resolution in Human Ear Recognition Using Eigenvalues
2018
2018 IEEE International Conference on Imaging Systems and Techniques (IST)
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Many techniques have been developed for ear recognition; however, most of the existing techniques have been tested on highresolution images taken in a laboratory environment. ...
PCA is then applied to the images, generating their eigenvalues, which are used as features for matching. ...
The number of features is then reduced through PCA. Both SVM and pairwise SVM were separately applied to the selected features to find the best match. ...
doi:10.1109/ist.2018.8577134
dblp:conf/ist/ZarachoffAM18a
fatcat:aut2n37b6vcydpwqqf5s5m557i
Unique Identification of Macaques for Population Monitoring and Control
[article]
2018
arXiv
pre-print
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Our primary contribution is a robust facial recognition and verification module designed for Rhesus macaques, but extensible to other non-human primate species. ...
In this work, we propose the Macaque Face Identification (MFID), an image based, non-invasive tool that relies on macaque facial recognition to identify individuals, and can be used to verify if they are ...
Deep Learning Approaches Freytag et al. use Convolutional Neural Networks (CNNs) for learning a feature representation of chimpanzee faces. ...
arXiv:1811.00743v2
fatcat:wdejenkcgrh7hnugyvt44cdnny
Biometrics Recognition Using Deep Learning: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1912.00271v2 fatcat:rre5cqafrzfrnpxma5b3ztglcmOther Versions
We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. ...
In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which ...
Nalini Ratha for reviewing this work, and providing very helpful comments and suggestions. ...
arXiv:1912.00271v3
fatcat:nobon7vrrrdnxe4pr3q2anl63y
Facial expression recognition using three-stage support vector machines
2019
Visual Computing for Industry, Biomedicine, and Art
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Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. ...
These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. ...
Generally, the main approaches were to do it via machine learning or deep learning. The majority of studies used the machine-learning approach, because deep learning is a recent trend. ...
doi:10.1186/s42492-019-0034-5
pmid:32240406
fatcat:c64b7q4mgjdjthjb7c6vd3sffu
Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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The automatically learned features by the GRBM are utilized in dividing the time-frequency units of the spectrographic mask into noise and speech dominant. ...
A missing data mask estimation method based on Gaussian-Bernoulli restricted Boltzmann machine (GRBM) trained on cross-correlation representation of the audio signal is presented in the study. ...
As an alternative to basing the multifeature approach on a set of "design" features, a GRBM [8] can be trained to learn the acoustical patterns for an arguably better performing set of features. ...
doi:10.1109/icassp.2013.6638964
dblp:conf/icassp/KeronenCRIP13
fatcat:bd6x3xppg5avbfpy6243w5anmu
A new distance measure for non-identical data with application to image classification
2017
Pattern Recognition
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2016 pre-print arXiv:1610.09766v1 fatcat:23iv3bne5vdzzpbl5q5g56uwfm
PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image ...
for different distributions in distance measures can improve performance in classification and recognition tasks. ...
Acknowledgments The authors thank Temasek Life Sciences Laboratory for funding this work and Entopsis LLC (Miami, FL) for providing all computer hardware used in this study. ...
doi:10.1016/j.patcog.2016.10.018
fatcat:o4jalrfayrf25fb46augimgozm
An overview of applications and advancements in automatic sound recognition
2016
Neurocomputing
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Similar to speech recognition systems, the robustness of an ASR system largely depends on the choice of feature(s) and classifier(s). ...
We also review techniques that have been utilized in noise robust sound recognition systems and feature optimization methods. ...
Deep neural networks While SVMs have seen an increased usage in ASR systems, a new machine learning algorithm called deep learning is generating a lot of interest in speech recognition. ...
doi:10.1016/j.neucom.2016.03.020
fatcat:cqaeimm5uvf3pajgozcrrcgxw4
Perceptual audio features for emotion detection
2012
EURASIP Journal on Audio, Speech, and Music Processing
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Starting from the outer and middle ear models of the auditory system, we base our features on the masked perceptual loudness which defines relatively objective criteria for emotion detection. ...
In this article, we propose a new set of acoustic features for automatic emotion recognition from audio. ...
Recently, a Generalized Discriminant Analysis (GerDa) method is proposed based on deep neural networks [9] . GerDa is able to learn 2D features extracted from 6552-dimensional openEAR features. ...
doi:10.1186/1687-4722-2012-16
fatcat:ukwp3h3s4fbgvlwvb34roe6ioq
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