Deep Periocular Recognition Method via Multi-Angle Data Augmentation

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COMPARISON OF INCPETION V3 AND MOBILENET V2 METHODS

Methods	Verification Accuracy	Test Accuracy	Model Size
IncpetionV3	98. 55%	98. 5%	93MB
MobileNetV2	98. 21%	98. 4%	24MB

AMPLIFIED SAMPLES OF THE DATA SET AROUND THE EYE

Eye Peripheral Data Set	Training Set	Verification Set	Test Set	Total sample	Sample Type
CASIA-Iris-Thousand	42000	14000	14000	70000	1000

PARAMETER SETTINGS

Parameter Types	Parameter Settings
Max number of steps	20000
Batch size	24
Learning rate	0. 001
Learning rate decay type	fixed
optimizer	RMSProp
Weight decay	0. 00004

OVERALL ARCHITECTURE OF MOBILENET V2

Input	Operator	t	c	n	s
224×224×3	Conv2d	-	32	1	2
112×112×32	Bottleneck	1	16	1	1
112×112×16	Bottleneck	6	24	2	2
56×56×24	Bottleneck	6	32	3	2
28×28×32	Bottleneck	6	64	4	2
14×14×64	Bottleneck	6	96	3	1
14×14×96	Bottleneck	6	160	3	2
7×7×160	Bottleneck	6	320	1	1
7×7×320	Conv2d 1×1	-	1280	1	1
7×7×1280	Avg pool 7×7	-		1	-
7×7×1280	Conv2d 1×1	-	1000	-

IMPLEMENTATION OF THE MOBILENET V2 CORE BUILDING MODULE

Input	Operator	Output
H×W×N	1×1 conv2d, ReLU6	H×W×t N
H×W×t N	3×3 dwise s=s, ReLU6	H/s ×W/s×t N
H s ×W s ×t N	linear 1×1 conv2d	H/s ×W/s ×t N

NETWORK MODEL PARAMETER SETTING

Parameter Types	Parameter Settings
Max number of steps	100000
Batch size	32
Learning rate	0. 001
Learning rate decay type	Fixed
optimizer	RMSProp
Weight decay	0. 00004

OVERALL STRUCTURE OF THE INCEPTION V3 NETWORK MODEL

Type	Size of Convolution Kernel/Step Size
convolution	3×3/2
convolution	3×3/1
convolution	3×3/1
pooling	3×3/2
convolution	3×3/1
convolution	3×3/2
convolution	3×3/1
Inception modules	3 Inception Module
Inception modules	3 Inception Module
Inception modules	3 Inception Module
pooling	8×8
linear	logits
Softmax	Classification of output

SAMPLE EYE PERIPHERAL DATASET

Eye Peripheral Data Set	Original Training Set	Raw Verification Set	Raw Test Set	Total Original Sample	Original Sample Type
CASIA-Iris-Thousand	6000	2000	2000	10000	1000