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°.
Highlights
Face recognition [1,2,3] has played an important role in the field of computer vision
Most facial recognition systems are designed with a Convolutional Neural Network (CNN) model, such as the Multitask Cascaded Convolutional Networks (MTCNNs) [4], Cascading networks [5,6,7], Fully Convolutional Networks (FCNs) [8,9,10,11], Feature Pyramid Networks (FPNs) [12,13], and Deep Convolutional Neural Networks (DCNNs) [14,15]
The network is constructed by FCN
Summary
Face recognition [1,2,3] has played an important role in the field of computer vision. DFEN utilizes a normal convolutional model to detect the rotation-invariant face from coarse to fine It changes the bounding box regression by introducing angle prediction processed by a Single Shot Detector (SSD). DFEN introduces an angle module to the network to extract the face angle features Their method achieves excellent accuracy in face detection, the detecting speed is not satisfactory due to the size of the SSD, which is almost 100 Megabytes.
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