Transfer Learning with Optimization Enabled Person Recognition using Ear Images


  • Sowmya M N Research Scholar, Channabasaveshwara Institute of Technology, Gubbi, Visvesvaraya Technological University, Belagavi
  • Keshava Prasanna Research Supervisor,Channabasaveshwara Institute of Technology, Gubbi, Visvesvaraya Technological University, Belagavi


Convolutional Neural Networks, Ear image, Person recognition, Transfer learning


The human ear is a good source of data for passive human recognition as it doesn't need the assistance of the individual whose identity we're trying to determine and because the ear's structure doesn't vary significantly over time. A hybrid method for person detection utilizing ear pictures called Gannet Sparrow Search Optimization _enabled Convolutional Neural Network with Transfer Learning (GSSO_CNN-TL) is created to counteract these constraints. The input ear image is initially attained from an ear recognition dataset and fed to pre-processing stage, where external noise are removed using Non-Local Means (NLM) filter. After that, techniques including rotation, flipping, cropping, and colour augmentation are applied to the pre-processed image. Then features like Scale-invariant feature transform (SIFT), Speeded up robust features (SURF), Pyramid Histogram of Oriented Gradients (PHoG), and Local Binary Pattern (LBP) are retrieved. Lastly, person identification is done using CNN with TL, where CNN is employed with hyper parameters from the trained models, namely deep batch-normalized eLU AlexNet (DbneAlexNet). Nonetheless, the GSSO is efficiently used to train classifier. By merging Gannet Optimization Algorithm (GOA) and Sparrow Search Algorithm (SSA), the created GSSO is produced. Furthermore, the devised approach achieved maximum precision, recall, and f-measure of 92.6%, 95.8%, and 94.2% respectively.


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How to Cite

M N, S. ., & Prasanna, K. . (2024). Transfer Learning with Optimization Enabled Person Recognition using Ear Images. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 66–76. Retrieved from



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