Abstract
Convolutional Neural Networks (CNN) are becoming increasingly popular in large-scale image recognition, classification, localization, and detection. Existing CNN models use the single model to extract the features and the recognition accuracy of these models is not adequate for real-time applications. In order to increase the recognition accuracy, an Ensemble of Convolutional Neural Networks (ECNN) based face recognition is proposed. The proposed model addresses the challenges of facial expression, aging, low resolution, and pose variations. The proposed ECNN model outperforms the existing state of the art models such as Inception-v3, VGG16, VGG19, Xception and ResNet50 CNN models with a Rank-5 accuracy of 97.12% on Web Face dataset and 100% on YouTube face dataset.
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Mohanraj, V., Sibi Chakkaravarthy, S., Vaidehi, V. (2019). Ensemble of Convolutional Neural Networks for Face Recognition. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_43
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DOI: https://doi.org/10.1007/978-981-13-1280-9_43
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