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Ear Recognition using Chainlet based Multi-Band SVM

Matthew Martin Zarachoff, Akbar Sheikh-Akbari, Dorothy Monekosso
2021 2021 IEEE International Conference on Imaging Systems and Techniques (IST)  
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

Matthew Martin Zarachoff, Akbar Sheikh-Akbari, Dorothy Monekosso
2022 Applied Sciences  
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

Ibrahim Omara, Hongzhi Zhang, Faqiang Wang, Ahmed Hagag, Xiaoming Li, Wangmeng Zuo
2018 Information  
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

Ibrahim Omara, Ahmed Hagag, Souleyman Chaib, Guangzhi Ma, Fathi E. Abd El-Samie, Enmin Song
2020 IEEE Access  
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

Keith April Araño, Peter Gloor, Carlotta Orsenigo, Carlo Vercellis
2021 Cognitive Computation  
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

Young Won Lee, Kang Ryoung Park
2022 Mathematics  
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

Eman M. Omran, Randa F. Soliman, Maryam Mostafa Salah, Sameh A. Napoleon, El-Sayed M. El-Rabaie, Mustafa M. AbdeElnaby, Nabil A. Ismail, Ayman A. Eisa, Fathi abd El-samie
2020 Menoufia Journal of Electronic Engineering Research  
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

Matthew Zarachoff, Akbar Sheikh-Akbari, Dorothy Monekosso
2018 2018 IEEE International Conference on Imaging Systems and Techniques (IST)  
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]

Ankita Shukla and Gullal Singh Cheema and Saket Anand and Qamar Qureshi and Yadvendradev Jhala
2018 arXiv   pre-print
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]

Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, David Zhang
2021 arXiv   pre-print
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

Issam Dagher, Elio Dahdah, Morshed Al Shakik
2019 Visual Computing for Industry, Biomedicine, and Art  
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

Sami Keronen, KyungHyun Cho, Tapani Raiko, Alexander Ilin, Kalle Palomaki
2013 2013 IEEE International Conference on Acoustics, Speech and Signal Processing  
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

Muthukaruppan Swaminathan, Pankaj Kumar Yadav, Obdulio Piloto, Tobias Sjöblom, Ian Cheong
2017 Pattern Recognition  
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

Roneel V. Sharan, Tom J. Moir
2016 Neurocomputing  
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

Mehmet Cenk Sezgin, Bilge Gunsel, Gunes Karabulut Kurt
2012 EURASIP Journal on Audio, Speech, and Music Processing  
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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