Abstract
Most of the emphasis of the study for neural networks on impaired face processing in autism has been on triplet-based autism/non-autism infant prediction. Children with ASD are most likely to be examined by their face’s individual components (such as, eyes, nose, and mouth), instead of combining the characteristics into a holistic face memory. Our proposed system identifies multiclass picture recognition by composing a GAN-based neural network and a deep CNN, preceded by L2 regularization and face embedding, thereby accurately predicting autistic children. In Autism infant detection, the Triplet Loss reduces the difference between an anchor and a positive with almost the same identity and significantly increases the variance between the source and a negative with a different label. The way we pick hard related triplets from inside the mini-batches is the key limitation in terms of batch size. We use a batch size of about 1800 examples in most studies. The GTLBFNet loss is based on the FaceNet model, which has training set containing three images of faces. Two of the images are of the same person (one is the Anchor and the other is the Positive), and the third picture is of a different person (Negative). Any representation of an autistic child's face is processed by the FaceNet model, which encodes the features in a 128-dimensional space and outputs a vector of size 128. The GTLBFaceNet model employs a GAN generator to precisely guess the expected outcomes. Our analysis accurately predicts autism dataset labels in 98.3% of cases.
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Joshua Samuel Raj, R., Anantha Babu, S., Jegatheesan, A., Arul Xavier, V.M. (2022). A GAN-Based Triplet FaceNet Detection Algorithm Using Deep Face Recognition for Autism Child. In: Peter, J.D., Fernandes, S.L., Alavi, A.H. (eds) Disruptive Technologies for Big Data and Cloud Applications. Lecture Notes in Electrical Engineering, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-19-2177-3_18
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DOI: https://doi.org/10.1007/978-981-19-2177-3_18
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