A Neural Network in Convolution with Constant Error Carousel Based Long Short Term Memory for Better Face Recognition

Abstract

Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial-related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with different commercial implementations, it nevertheless faces problems in certain real-world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video compression artefacts, and so on. To solve the problems described above, a face detection technique called Convolution Neural Network with Constant Error Carousel dependent Long Short Term Memory (CNN-CEC-LSTM) is proposed in this paper. This research implemented a novel network structure and designed a special feature extraction that employs a self-channel attention (SCA) block and a self-spatial attention (SSA) block that adaptively aggregates the feature maps in both channel and spatial domains to learn the inter-channel and inter-spatial connection matrices; additionally, matrix multiplications are conducted for a This approach first smoothed the initial image with a Gaussian filter before measuring the gradient image. The Canny-Kirsch Method edge detection algorithm was then used to identify human face edges. The proposed method is evaluated against two recent difficult face detection databases, including the IIT Kanpur Dataset. The experimental findings indicate that the proposed approach outperforms the most current cutting-edge face recognition approaches.

Intelligent Education Managemnt System Design for Universities based on MTCNN Face Recognition Algorithm

“…The calculation formula is shown in equation 3. 13. Next, a grid search method is used to systematically explore various combinations of hyper-parameters [17].…”

“…Self-channel attention was utilized to fuse feature maps in both channel and space. The experimental results showed that this face detection technology was superior to other face recognition models [13]. Sun et al proposed an English pedagogy model to enhance students' learning efficiency.…”

A face recognition method based on sparse enhancement and collaborative information fusion for epidemic prevention and control

“…The experiments show that the RSCPA algorithm can obtain sparser vectors with the same performance as the PCA algorithm, demonstrating the superiority of the algorithm in face recognition and handwritten digit recognition [2].Ramaraj P To address severe facial occlusions, incredibly low resolution, strong illumination, unusual pose inconsistencies, picture or video compression artefacts, etc., it is in some realistic scenarios still face problems,adaptively aggregated channel and spatial domain Self-Confident Channel Attention (SCA) blocks and Self-Spatial Attention (SSA) blocks are used, followed by the Canny Kirsch method edge detection algorithm to identify face edges. Experimental results show that the proposed method outperforms the current state-of-the-art face recognition methods [3]. All of the above-mentioned technical approaches have shown different kinds of shortcomings in the face of the special circumstances of epidemic prevention and control, and are unable to meet the needs of face recognition at present.…”