Three dimensional objects recognition & pattern recognition technique; related challenges: A review
S Rani, K Lakhwani, S Kumar - Multimedia Tools and Applications, 2022 - Springer
Abstract 3D object recognition and pattern recognition are active and fast-growing research
areas in the field of computer vision. It is mandatory to define the pattern class, feature …
areas in the field of computer vision. It is mandatory to define the pattern class, feature …
Face recognition: challenges, achievements and future directions
M Hassaballah, S Aly - IET Computer Vision, 2015 - Wiley Online Library
Face recognition has received significant attention because of its numerous applications in
access control, law enforcement, security, surveillance, Internet communication and …
access control, law enforcement, security, surveillance, Internet communication and …
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective
protection methods is wearing a face mask in public areas according to the World Health …
protection methods is wearing a face mask in public areas according to the World Health …
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …
processing, image processing, computer vision, and pattern recognition. Sparse …
Sparse representation or collaborative representation: Which helps face recognition?
As a recently proposed technique, sparse representation based classification (SRC) has
been widely used for face recognition (FR). SRC first codes a testing sample as a sparse …
been widely used for face recognition (FR). SRC first codes a testing sample as a sparse …
Discriminative transfer subspace learning via low-rank and sparse representation
In this paper, we address the problem of unsupervised domain transfer learning in which no
labels are available in the target domain. We use a transformation matrix to transfer both the …
labels are available in the target domain. We use a transformation matrix to transfer both the …
Synthesized feature based few-shot class-incremental learning on a mixture of subspaces
A Cheraghian, S Rahman… - Proceedings of the …, 2021 - openaccess.thecvf.com
Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes
to a well-trained base model in multiple training sessions with the restriction that only a few …
to a well-trained base model in multiple training sessions with the restriction that only a few …
Maximum correntropy criterion for robust face recognition
In this paper, we present a sparse correntropy framework for computing robust sparse
representations of face images for recognition. Compared with the state-of-the-art l 1 norm …
representations of face images for recognition. Compared with the state-of-the-art l 1 norm …
Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes
Recently, regression analysis has become a popular tool for face recognition. Most existing
regression methods use the one-dimensional, pixel-based error model, which characterizes …
regression methods use the one-dimensional, pixel-based error model, which characterizes …
Inter-class sparsity based discriminative least square regression
Least square regression is a very popular supervised classification method. However, two
main issues greatly limit its performance. The first one is that it only focuses on fitting the …
main issues greatly limit its performance. The first one is that it only focuses on fitting the …