Head Pose Estimation Based on Multi-Scale Convolutional Neural Network

Lingyu Liang; Tiantian Zhang; Wei He

doi:10.3788/LOP56.131003

Laser & Optoelectronics Progress, Volume. 56, Issue 13, 131003(2019)

Head Pose Estimation Based on Multi-Scale Convolutional Neural Network

Lingyu Liang^1,2,3、**, Tiantian Zhang^1,3, and Wei He^1、*

¹ Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

² School of Information Science and Technology, ShanghaiTech University, Shanghai 200120, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract Get PDF(in Chinese)

Figures & Tables(10)

Fig. 1. Head pose estimation flow chart

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Fig. 2. Deep neural network structure for head pose estimation. (a) Multi-scale convolution structure; (b) process structure after feature combination

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Fig. 3. Part of the experimental head posture library picture. (a) CAS-PEALR1; (b) Pointing'04

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Fig. 4. Face images after cropped

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Fig. 5. Partial head posture pictures under different interference factors. (a) Standard; (b) with mask; (c) with glasses; (d) expression; (e) weak illumination; (f) strong illumination; (g) background

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Table 1. Multi-scale convolution vs. single-scale convolution
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Table 1. Multi-scale convolution vs. single-scale convolution
Convolution Recognition accuracyon test set /%
Multi-scale convolution 98.9
3×3 single-scale convolution 94.3
5×5 single-scale convolution 93.1
7×7 single-scale convolution 93.2

Table 2. Accuracy of different algorithms on Pointing'04 and CAS-PEAL-R1
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Table 2. Accuracy of different algorithms on Pointing'04 and CAS-PEAL-R1
Algorithm Pointing'04accuracy /% CAS-PEAL-R1 accuracy /% Numberofgestures
Algorithm ofthis paper 96.5 98.9 21
Cluster-glassificationBayesian network^[23] 94.8 96.2 12
Based onfacialfeature points^[24] 92.7 93.5 6

Table 3. Gestures Conversion relationship table

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Table 3. Gestures Conversion relationship table

Aftergesturesconversion	Pitch attitudebefore gesturesconversion	Yaw attitudebefore gesturesconversion /(°)
Level	PM	0,-15,+15
Level left	PM	+30,+45
Level right	PM	-30,-45
Pitch down	PD	0,-15,+15
Pitch up	PU	0,-15,+15
Left up	PU	+30,+45
Right up	PU	-30,-45
Left down	PD	+30,+45
Right down	PD	-30,-45

Table 4. Different interference factors effect on recognition rate

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Table 4. Different interference factors effect on recognition rate

Interferencefactor	Accuracy ofthis paper /%	Accuracy ofmethod in Ref.[23] /%
Standard	98.5	96.3
With mask	92.9	86.4
With glasses	96.3	89.7
Expression	98.1	94.9
Weak illumination	95.5	91.8
Strong illumination	96.1	92.2
Background	97.7	95.1

Table 5. Recognition time at different resolution
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Table 5. Recognition time at different resolution
Resolution /(pixel×pixel) Timeof thispaper /ms Timeof method inRef. [23] /ms Timeof method inRef. [24] /ms
1920×1080 34.3 51.3 562.1
1360× 760 32.7 48.5 451.7
800×600 30.8 45.2 349.8

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Lingyu Liang, Tiantian Zhang, Wei He. Head Pose Estimation Based on Multi-Scale Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131003

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