A Survey of Unconstrained Face Recognition Algorithm and Its Applications

Tyagi, Ranbeer; Tomar, Geetam Singh; Baik, Namkyun

doi:10.14257/ijsia.2016.10.12.30

Cited by 3 publications

(1 citation statement)

References 16 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A survey of unconstrained face recognition algorithm and its applications [53] Discusses face recognition techniques in terms of their behavior at pose variations, non-uniform motion blur and illumination for the period 2011-2014. The discussion includes several neural network based systems.…”

mentioning

confidence: 99%

Deep-learned faces: a survey

Arachchilage

Izquierdo

2020

J Image Video Proc.

View full text Add to dashboard Cite

Deep learning technology has enabled successful modeling of complex facial features when high-quality images are available. Nonetheless, accurate modeling and recognition of human faces in real-world scenarios “on the wild” or under adverse conditions remains an open problem. Consequently, a plethora of novel deep network architectures addressing issues related to low-quality images, varying pose, illumination changes, emotional expressions, etc., have been proposed and studied over the last few years.This survey presents a comprehensive analysis of the latest developments in the field. A conventional deep face recognition system entails several main components: deep network, optimization loss function, classification algorithm, and train data collection. Aiming at providing a complete and comprehensive study of such complex frameworks, this paper first discusses the evolution of related network architectures. Next, a comparative analysis of loss functions, classification algorithms, and face datasets is given. Then, a comparative study of state-of-the-art face recognition systems is presented. Here, the performance of the systems is discussed using three benchmarking datasets with increasing degrees of complexity. Furthermore, an experimental study was conducted to compare several openly accessible face recognition frameworks in terms of recognition accuracy and speed.

show abstract

mentioning

confidence: 99%