3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

H, Zou; Sun, Xiaohui

doi:10.3390/electronics10202539

Cited by 10 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hu et al [24] leveraged the 3D spatial structure of the face and combined it with bidirectional long short-term memory (BLSTM) layers to estimate head poses in naturalistic driving conditions. Considering that the point clouds lack texture, Zou et al [25] combined gray images and proposed a sparse loss function for 3D face recognition. Recently, Ma et al [26] combined PointNet and deep regression forests to construct a new deep learning method in order to improve the efficiency of the head pose estimations.…”

Section: Related Workmentioning

confidence: 99%

Siamese PointNet: 3D Head Pose Estimation with Local Feature Descriptor

2023

View full text Add to dashboard Cite

Head pose estimation is an important part of the field of face analysis technology. It can be applied to driver attention monitoring, passenger monitoring, effective information screening, etc. However, illumination changes and partial occlusion interfere with the task, and due to the non-stationary characteristic of the head pose change process, normal regression networks are unable to achieve very accurate results on large-scale synthetic training data. To address the above problems, a Siamese network based on 3D point clouds was proposed, which adopts a share weight network with similar pose samples to constrain the regression process of the pose’s angles; meanwhile, a local feature descriptor was introduced to describe the local geometric features of the objects. In order to verify the performance of our method, we conducted experiments on two public datasets: the Biwi Kinect Head Pose dataset and Pandora. The results show that compared with the latest methods, our standard deviation was reduced by 0.4, and the mean error was reduced by 0.1; meanwhile, our network also maintained a good real-time performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Siamese PointNet: 3D Head Pose Estimation with Local Feature Descriptor

2023

View full text Add to dashboard Cite

show abstract

“…In recent years, deep convolution neural network has made great progress in sound recognition and other aspects [11], [12], [13]. Zhang et al [14] used short time Fourier transform (STFT) and other methods to convert birds sound into the spectrum and used convolutional neural network to classify bird sounds.…”

Section: A Prior Workmentioning

confidence: 99%

Design of Bird Sound Recognition Model Based on Lightweight

Yang

Jiang

2022

IEEE Access

View full text Add to dashboard Cite

Bird sounds recognition is of great significance in bird protection. With appropriate sound classification, research can automatically predict the quality of life in the area. Nowadays, the deep learning model is used to classify bird sound data with high classification accuracy. However, the generalization ability of most existing bird sound recognition models is poor, and the complicated algorithm is applied to extract bird sound features. To address these problems, a large data set containing 264 kinds of birds is constructed in this paper to enhance the generalization ability of the model, and then a lightweight bird sound recognition model is proposed to build a lightweight feature extraction and recognition network with MobileNetV3 as the backbone. By adjusting the depthwise separable convolution in the model, the recognition ability of the model is improved. A multi-scale feature fusion structure is designed, and the PSA (Pyramid Split Attention, PSA) module is added to the multi-scale feature fusion structure to improve the adaptability of the network to scale extraction of spatial information and channel information. To improve the refinement ability of the model towards the global information, the channel attention mechanism and ordinary convolution are introduced into Bneck module which makes the Bneck module become the Bnecks module. The experimental results show that the accuracy of Top-1 and Top-5 of the model in identifying 264 kinds of birds on the self-built data set is 95.12% and 100%, which are higher than that of MobileNetV1, MobileNetV2, MobileNetV3 respectively. Although the accuracy is lower than ResNet50, the number of parameters and floating-point operations (FLOPs) of the model is only 2.6M and 127M respectively. The accuracy is only reduced by 2.25% while saving costs.

show abstract

“…Compared to traditional plant segmentation methods, DL approaches exhibit significant advantages in various aspects, including speed and accuracy [20][21][22][23]. Expanding DL from two-dimensional (2D) to threedimensional (3D) research applications is currently a trending research direction [24,25]. Utilizing machine vision techniques to acquire image-based 3D models of seedlings offers several advantages.…”

Section: Introductionmentioning

confidence: 99%

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

Li,

Xia,

Liu

et al. 2023

Forests

View full text Add to dashboard Cite

Pinus massoniana (Lamb.) is an important plantation species in southern China. Accurate measurement of P. massoniana seedling morphological indicators is crucial for accelerating seedling quality assessment. Machine vision, with its objectivity and stability, can replace human eyes in performing these measurements. In this paper, a measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed. Firstly, multi-angle sequence images of 30 one-year-old P. massoniana seedlings were collected, distorted, and corrected to generate a sparse point cloud through the Structure-from-Motion (SFM) and dense point cloud through the Patch-Based Multiple View Stereo (PMVS). Secondly, a Dense Weighted Semantic Segmentation Model based on PointNet++ was designed, achieving effective segmentation of the P. massoniana seedling point clouds. Finally, a multi-iteration plane method based on Laplacian contraction was proposed. The new skeleton points were refined by minimizing the Euclidean distance, iteratively generating the optimal morphological skeleton, thus facilitating the extraction of morphological indicators. The experimental results demonstrated a good correlation between the machine vision-extracted morphological indicators (including plant height, ground diameter, and height-to-diameter ratio) and manually measured data. The improved PointNet++ model achieved an accuracy of 0.9448 on the training set. The accuracy and Mean Intersection over Union (MIoU) of the test set reached 0.9430 and 0.7872, respectively. These findings can provide reliable technical references for the accurate assessment of P. massoniana seedling quality and the promotion of digital forestry construction.

show abstract

3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

Cited by 10 publications

References 35 publications

Siamese PointNet: 3D Head Pose Estimation with Local Feature Descriptor

Siamese PointNet: 3D Head Pose Estimation with Local Feature Descriptor

Design of Bird Sound Recognition Model Based on Lightweight

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

Contact Info

Product

Resources

About