This paper presents a biometric recognition based on the iris of a human eye using gray-level co-occurrence matrix (GLCM). A new approach of GLCM, called 3D-GLCM, which is expanded from the original 2D-GLCM is proposed and used to extract the iris features. The experimental results show that the proposed approach gains an encouraging performance on the UBIRIS iris database. The recognition rate up to 99.65% can be achieved.
Automatically describing the content of an image is an interesting and challenging task in artificial intelligence. In this paper, an enhanced image captioning model—including object detection, color analysis, and image captioning—is proposed to automatically generate the textual descriptions of images. In an encoder–decoder model for image captioning, VGG16 is used as an encoder and an LSTM (long short-term memory) network with attention is used as a decoder. In addition, Mask R-CNN with OpenCV is used for object detection and color analysis. The integration of the image caption and color recognition is then performed to provide better descriptive details of images. Moreover, the generated textual sentence is converted into speech. The validation results illustrate that the proposed method can provide more accurate description of images.
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