SACN: A Novel Rotating Face Detector Based on Architecture Search

Abstract

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6 to 10.5.

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

“…The ORB algorithm can handle rotated images since it estimates each corner's orientation based on the intensity distribution around the corner (Song, Xu, & Zhai, 2021). Due to the speed and resistance to changes in lighting and viewpoint, the ORB features are frequently utilized in computer vision applications like object detection and tracking (Song, Xu, & Zhai, 2021). The ORB algorithms are constrained under challenging conditions of lighting at the fixed threshold, as such it unable to extract feature points, Li, Zeng, Shan, & Chen (2018) employs the technique to enhance the ORB algorithm for feature extraction, which combines picture enhancement with shortened adaptive threshold and the original image is turned into a grayscale version and the image is improved using unsharp masking, truncated adaptive gamma brightness correction, and Gaussian filtering for noise reduction.…”

“…The ORB algorithm can handle rotated images since it estimates each corner's orientation based on the intensity distribution around the corner (Song, Xu, & Zhai, 2021). Due to the speed and resistance to changes in lighting and viewpoint, the ORB features are frequently utilized in computer vision applications like object detection and tracking (Song, Xu, & Zhai, 2021).…”