Identification and Authentication Using Blink with Smart Glasses

1. Identification and Authentication
Using Blink with Smart Glasses
K e i o U n i v e r s i t y
Yohei Kawasaki and Yuta Sugiura
The SICE Annual Conference 2022

2. • Widespread of Smart Glasses
• Google Glass
• JINS MEME
• Growing market size
• Need for Identification and Authentication
2
[1] Google「Glass Enter Edition2」， https://www.nttdocomo.co.jp/biz/product/xr/googleglass/， (Accessed August 19th, 2022)
[2] JINS MEME，https://www.jins.com/jp/item/WCE-19S-001.html， (Accessed August 19th, 2022)
Widespread of Smart Glasses
Google Glass[1] JINS MEME[2]

3. 3
Type of Authentication
Passwords, PINs IC cards, Keys
Knowledge-Based Authentication Possession-Based Authentication
Biometric Authentication
Irises, Fingerprint Gait, Handwriting
Physical Characteristics Behavioral Characteristics

4. • Using blink as a behavioral feature
4
Blink as Behavior for Authentication

5. • With a head-mounted device
5
Related Work: Identification and Authentication Method
with Head Mounted Device
• Using blink
Acoustic properties of the nose
[Isobe, 2021]
Acoustic properties of the skull
[Schneegass, 2016]
Electro-Encephalo-Gram (EEG)
[Abo-Zahhad, 2014]
Blink pattern
[Westeyn, 2004]
Head shape
[Fujii, 2021]

6. • Detecting blink by photo-reflectors mounted on smart glasses for
Identification and Authentication
• Contactless
• Low cost
• Small size
• Hands-free
6
Method

7. 7
Implementation: Hardware
Get sensor value with
photo-reflectors
Transmit to computer
via Arduino UNO
Analysis

8. • Identification
• Predicts which class the input data belong to
• Evaluated by accuracy
• Authentication
• Predicts whether the input data is the person or not
• Evaluated by
• FRR (False Rejection Rate)
• FAR (False Acceptance Rate)
• EER (Equal Error Rate)
8
Identification and Authentication

9. • Estimates the class with the smallest DTW distance as the correct class.
• Participants: 𝐴𝑘 (𝑘 = 1, 2, … 𝑁)
9
Identification Method

10. • Consider the authentication of 𝐴𝑘 as an example
• Participants: 𝐴𝑘 (𝑘 = 1, 2, … 𝑁)
• Calculate Mahalanobis distance to only class k data
10
Authentication Method

11. • 𝐹𝑅𝑅 =
𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒+𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
• 𝐹𝐴𝑅 =
𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒+𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
11
Authentication Method
θ: threshold

12. • Set a threshold for accepting that
it is the person.
• FRR (False Rejection Rate)
• FAR (False Acceptance Rate)
• EER (Equal Error Rate)
12
Authentication Method
EER

13. • Prompts participants to blink in 2 patterns.
• Pattern 1: Blink 5 times in a row at any time.
• Pattern 2: Blink 5 times in a row at a tempo of 120 BPM.
• 20 measurements each
• Frame rate: 70fps
• 10-fold cross validation with 140 data
13
Number of participants 7
Sex 5 male, 2 female
Mean of Age 26.6
SD of Age 12.4
Experiment
Participants information
Photo-reflectors

14. 14
Result: Identification
Blink pattern Accuracy (%)
Pattern 1 88.6
Pattern 2 97.1
Each accuracy
Confusion matrix of pattern 1 Confusion matrix of pattern 2

15. 15
Result: Authentication
Blink pattern EER (%)
Pattern 1 8.6
Pattern 2 6.4
Each EER
FRR and FAR of pattern 1 FRR and FAR of pattern 2

16. Limitation
• Sensor value changes depending on sunlight
• Setting the mounting position
Future Work
• Expansion of the range of participants
• Develop robustness
• Change of mounting position
• Measurements of different days
16
Limitation and Future Work

17. 17
Summary
Background Identification and Authentication with Smart Glasses
Related work
Identification and Authentication with head mounted
device and use of blink
Suggestion Use blink as Behavioral Characteristics
Implementation Sensing blink with photo reflector
Evaluation
10-fold cross validation
Identification: accuracy Authentication: FRR, FAR and EER
Result Identification: 97.1 % Authentication: 6.4 %
Limitations Mounting position, effects of sunlight
Future works
Change of mounting position and Measurements of
different days to develop robustness