Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. Marsico, M. Nappi, D. Riccio, H. Wechsler (2017)
Leveraging implicit demographic information for face recognition using a multi-expert systemMultimedia Tools and Applications, 76
B. Rajakumar (2012)
The Lion's Algorithm: A New Nature-Inspired Search AlgorithmProcedia Technology, 6
Cunrui Wang, Cunrui Wang, Qingling Zhang, Wanquan Liu, Yu Liu, Lixin Miao (2019)
Facial feature discovery for ethnicity recognitionWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10
Lindsey Short, C. Mondloch, C. McCormick, J. Carré, Ruqian Ma, Genyue Fu, Kang Lee (2012)
Detection of Propensity for Aggression based on Facial Structure Irrespective of Face Race.Evolution and human behavior : official journal of the Human Behavior and Evolution Society, 33 2
Iris Blandón-Gitlin, K. Pezdek, S. Saldivar, Erin Steelman (2014)
Oxytocin eliminates the own-race bias in face recognition memoryBrain Research, 1580
Thanh-Hung Vo, Trang Nguyen, C. Le (2018)
Race Recognition Using Deep Convolutional Neural NetworksSymmetry, 10
Tarik Alafif, Zeyad Hailat, M. Aslan, Xue-wen Chen (2017)
On Classifying Facial Races with Partial Occlusions and Pose Variations2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
A. Proverbio, V. Gabriele (2017)
The other-race effect does not apply to infant faces: An ERP attentional studyNeuropsychologia, 126
Lun Zhao, S. Bentin (2011)
The role of features and configural processing in face-race classificationVision Research, 51
H. Wiese, S. Schweinberger (2018)
Inequality between biases in face memory: Event-related potentials reveal dissociable neural correlates of own-race and own-gender biasesCortex, 101
S. Mirjalili (2016)
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Computing and Applications, 27
J. Cavazos, E. Noyes, A. O’Toole (2019)
Learning context and the other-race effect: Strategies for improving face recognitionVision Research, 157
Lulu Wan, Kate Crookes, Katherine Reynolds, Jessica Irons, Elinor McKone (2015)
A cultural setting where the other-race effect on face recognition has no social–motivational component and derives entirely from lifetime perceptual experienceCognition, 144
Feng Cheng, Shilin Wang, Xizi Wang, Alan Liew, Gongshen Liu (2019)
A global and local context integration DCNN for adult image classificationPattern Recognit., 96
Valentina Proietti, Sarah Laurence, Claire Matthews, Xiaomei Zhou, C. Mondloch (2019)
Attending to identity cues reduces the own-age but not the own-race recognition advantageVision Research, 157
A. Boyseens, Serestina Viriri (2016)
Component-Based Ethnicity Identification from Facial Images
(2018)
July. Race classification from face using deep convolutional neural networks
WIREs Data Mining and Knowledge Discovery, 9
Rajakumar Boothalingam (2018)
Optimization using lion algorithm: a biological inspiration from lion’s social behaviorEvolutionary Intelligence, 11
Fakhri Shafai, Ipek Oruc (2018)
Qualitatively similar processing for own- and other-race faces: Evidence from efficiency and equivalent input noiseVision Research, 143
P. Hills, J. Pake (2013)
Eye-tracking the own-race bias in face recognition: Revealing the perceptual and socio-cognitive mechanismsCognition, 129
B. Marsh, K. Pezdek, Daphna Ozery (2016)
The cross-race effect in face recognition memory by bicultural individuals.Acta psychologica, 169
Xiaoguang Lu, Hong Chen, Anil Jain (2006)
Multimodal Facial Gender and Ethnicity Identification
Rishi Gupta, Sandeep Kumar, Pradeep Yadav, Sumit Shrivastava (2018)
Identification of Age, Gender, & Race SMT (Scare, Marks, Tattoos) from Unconstrained Facial Images Using Statistical Techniques2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE)
X. Ding, Genyue Fu, Kang Lee (2014)
Neural correlates of own- and other-race face recognition in children: A functional near-infrared spectroscopy studyNeuroImage, 85
Kathleen Hourihan, A. Benjamin, Xiping Liu (2012)
A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race.Journal of applied research in memory and cognition, 1 3
Md. Uddin, M. Hassan, Ahmad Almogren, M. Zuair, G. Fortino, J. Tørresen (2017)
A facial expression recognition system using robust face features from depth videos and deep learningComput. Electr. Eng., 63
Changjun Zhou, Lan Wang, Qiang Zhang, Xiaopeng Wei (2013)
Face recognition based on PCA image reconstruction and LDAOptik, 124
I. Thornton, Duangkamol Srismith, M. Oxner, W. Hayward (2019)
Other-race faces are given more weight than own-race faces when assessing the composition of crowdsVision Research, 157
B. Rajakumar (2020)
Lion Algorithm and Its Applications
Sasan Karamizadeh, Shahidan Abdullah (2018)
Race classification using gaussian-based weight K-nn algorithm for face recognitionThe Journal of Engineering Research, 6
Gang Sun, Luping Song, S. Bentin, Yanjie Yang, Lun Zhao (2013)
Visual search for faces by race: A cross-race studyVision Research, 89
Qizhi Yao, Lun Zhao (2019)
Using spatial frequency scales for processing own-race and other-race faces: An ERP analysisNeuroscience Letters, 705
Jie Yuan, Xiaoqing Hu, Yuhao Lu, G. Bodenhausen, Shimin Fu (2017)
Invisible own- and other-race faces presented under continuous flash suppression produce affective response biasesConsciousness and Cognition, 48
B. Rajakumar (2014)
Lion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion's social behavior2014 IEEE Congress on Evolutionary Computation (CEC)
Xiangnan Zhang, Xinbo Gao, Chunna Tian (2018)
Text detection in natural scene images based on color prior guided MSERNeurocomputing, 307
Simone Tüttenberg, H. Wiese (2019)
Learning own- and other-race facial identities: Testing implicit recognition with event-related brain potentialsNeuropsychologia, 134
The humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA).FindingsThe performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively.Originality/valueThis paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.
International Journal of Intelligent Computing and Cybernetics – Emerald Publishing
Published: Aug 21, 2020
Keywords: Facial race detection; Color dense features; DBN; Optimization; LMUDA
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.