Automated Facial Recognition in Older Photographs Using One-Shot Learning in Siamese Networks and Transfer Learning
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Abstract
A lot of historical information comes in various forms, such as old documents, papers, photographs, videos, audio, and even artefacts and sculptures. Photographs, audio, and videos are especially important because they effectively convey information. When we convert these into digital versions, it becomes easy to share, access online or offline, copy, move around, back up, and store in numerous places. However, a challenge with digital content is that it is often difficult to search due to the absence of readable text. Consequently, we cannot effectively analyse and utilise critical information. To make it useful, we manually look at pictures and add tags to create labels. While basic labels suffice for most searches, it becomes more complicated when dealing with a large number of photographs. Enhancements in search capabilities are needed to make the process easier, quicker, and more efficient. Fortunately, recent technological advances, such as artificial intelligence, provide us with facilities to simplify this process. This paper explores how artificial intelligence can streamline this process, enhancing search efficiency and enabling automatic identification and tagging of individuals in photos, thus facilitating easier access and analysis of digital archives.
It is anticipated that manual tagging efforts could be reduced by approximately 80%, and the searchability of photographs could be enhanced by about 84%.
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