Face Recognition Deep Learning Papers

36 papers with code computer vision.

Face recognition deep learning papers. Web scale training for face identification yaniv taigman et al 2015. Starting in the seventies face recognition has become one of the most researched topics in computer vision and biometrics. With the deep learning in different areas of success beyond the other methods set off a new wave of neural network development. In this paper we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with softmax regress dbnesr as a natural source for obtaining additional complementary hierarchical representations which helps to relieve us.

Increasingly packing multiple facial informatics modules in a unified deep learning model via lifelong learning. Awesome deep learning papers for face recognition. Research on face recognition based on deep learning abstract. Facial recognition is the task of making a positive identification of a face in a photo or video image against a pre existing database of faces.

Motivated by the nature of human learning that easy cases are learned firstandthencomethehardones 2 ourcurricularfacein corporates the idea of curriculum learning cl into face recognition in an adaptive manner which differs from the. Subsequent works have explored different loss functions to improve the discrimination power of the feature. Most modern face recognition and classification systems mainly rely on hand crafted image feature descriptors. The state of the art tables for this task are contained mainly in the consistent parts of the task.

Traditional methods based on hand crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. The 2018 paper titled deep face recognition. A survey provides a helpful summary of the state of face recognition research over the last nearly 30 years highlighting the broad trend from holistic learning methods such as eigenfaces to local handcrafted feature detection to shallow learning methods to finally deep learning methods that are currently state of the art. It begins with detection distinguishing human faces from other objects in the image and then works on identification of those detected faces.

The concept of deep learning originated from the artificial neural network in essence refers to a class of neural networks with deep structure of the effective training methods 1. Learning loss termed curricularface to achieve a novel training strategy for deep face recognition. 36 propose an early deep convolutional neural network for face recognition. Thus we trained it on the largest facial dataset to date an identity labeled dataset of four million facial images belonging to more than 4 000 identities.

Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing rather than the standard convolutional layers.