Domain Specific Learning for Newborn Face Recognition PROJECT TITLE :Domain Specific Learning for Newborn Face RecognitionABSTRACT:Biometric recognition of newborn babies is an opportunity for the conclusion of many helpful applications, such as improved security against swapping and abduction, correct census, and effective drug delivery. This paper explores the possibility of using face recognition toward an inexpensive and friendly biometric modality for newborns. The paper proposes an autoencoder-based mostly feature representation followed by downside specific distance metric learning via one-shot similarity with one category-online support vector machine. The largest publicly out there database of newborns collected from varied sources to check face recognition is introduced. Many existing face recognition approaches and business systems are also evaluated on a common benchmark protocol. The efficacy of the proposed algorithm is evaluated beneath both verification and identification settings. With multiple galleries, rank-1 identification accuracy of 78.five% and verification accuracy of 63.four% at 0.1% false settle for rate are achieved. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A Quasi Random Symbol Interleaving Technique Applied to Image Transmission By Noisy Channels Hearing Protection: The Electrical Hazard You Don't Hear About
