Compact Convolutional Neural Networks for Pterygium Classification using Transfer Learning Pterygium is one of the eye diseases that are prevalent among the workers who are frequently exposed to sunlight such as farmers, construction workers and fishermen. It is an eye disease that can be screened easily by health practitioners. However, most of the previously mentioned workers are rarely aware of the pterygium existence. Hence, health practitioners usually visit them to perform basic health checkup that includes eye disease screening. Due to many tests that need to be performed for each checkup cycle, a modern tool is required to make the screening process faster. As a result, this paper discusses a compact convolutional neural networks (CNN) approach to pterygium classification using transfer learning. Its architecture consists of only two layers of CNN and two layers of fully connected (FC) components. Rectified linear unit is used as the activation function except for the last FC layer, which utilizes Softmax function. The weights and biases are transferred from the first two layers of VGGM architecture. Three regularization methods were experimented that include local response normalization, batch normalization and dropout. The best network configuration in term of accuracy with 0.9833 value is obtained by using local response normalization with dropout. On the other hand, the best area under the curve performance of 0.9865 is achieved by using batch normalization and dropout. Therefore, the developed compact system has managed to classify well the pterygium image taken from the frontal view.

Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dualstream Convolutional Neural Network In spite of the advancements made in the periocular recognition, the dataset and periocular recognition in the wild remains a challenge. In this paper, we propose a multilayer fusion approach by means of a pair of shared parameters (dualstream) convolutional neural network where each network accepts RGB data and a novel colourbased texture descriptor, namely Orthogonal CombinationLocal Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. Specifically, two distinct latefusion layers are introduced in the dualstream network to aggregate the RGB data and OCLBCP. Thus, the network beneficial from this new feature of the latefusion layers for accuracy performance gain. We also introduce and share a new dataset for periocular in the wild, namely Ethnicocular dataset for benchmarking. The proposed network has also been assessed on one publicly available dataset, namely UBIPr. The proposed network outperforms several competing approaches on these datasets.