This document presents a method for face detection and gender recognition using data science. It introduces the importance of estimating age and gender from facial images for applications like access control and surveillance. The method uses mean absolute error and cumulative score to measure age estimation accuracy. It then describes the steps of the method which include segmenting the input image pixels, removing non-face objects, separating connected components, identifying components as faces or non-faces, refining face positions, finding faces through template matching, and detecting gender based on mean intensity and template matching. Test results on sample images are presented showing high accuracy of face detection and low false positive rates.

1. DATA SCIENCE IN
FACE DETECTION
AND GENDER
RECOGNITION
PRESENTED BY:
M.SRINIVAS
(19D41A05D3)
CSE-C

2. INTRODUCTION
Age and gender, two of the key facial attributes, play
a very foundational role in social interactions, making
age and gender estimation from a single face image
an important task in intelligent applications, such as
access control, human-computer interaction, law
enforcement, marketing intelligence, and visual
surveillance, etc.
The face age estimation algorithm mainly uses
mean absolute error (MAE) and cumulative score
(CS) as the standard to measure the accuracy of age
estimation

3. EMPIRICAL PDF APPROXIMATION

4. ADVANTAGES
• Fast recognition than human.
• No human work needed.
• Time is lesser to recognize(within seconds).
• Large amount of data is easily identified.
• Convenient than biometric technology.

5. INPUT IMAGE

6. PIXEL SEGMENTATION
USING THE RGB PIXEL PDF

7. NON-FACE OBJECT REMOVAL

8. SIZE-BASED
NON-FACE OBJECT REMOVAL

9. LOCATION-BASED
NON-FACE OBJECT REMOVAL

10. PCA-BASED
NON-FACE OBJECT REMOVAL

11. CONNECTED COMPONENTS

12. COMPONENT SEPARATION

13. SEPARATED COMPONENTS

14. COMPONENT IDENTIFICATION
• Template matching
and peak
thresholding to
remove remaining
non-face objects
• Removal of
repeated faces
segments using a
distance constraint

15. FACE POSITION REFINEMENT
• The face centre is located at the bridge of the
nose
• The centroid of the segmented face is
somewhat inaccurate in finding face centres
• Multi-scale, high threshold template matching
finds centres more accurately
• Use centroid for remaining faces

16. FINDING FACES
WITH TEMPLATE MATCHING
• High threshold for
accurate centre
location
• Moderate threshold
for robust backup
face location
• if morphological
subsystem gives
unexpected results

17. GENDER DETECTION
• Mean intensity
• Template matching
using average of
each female face
• Biased towards
missing female
faces to avoid
false-positive
penalty (9:1)

18. FACE DETECTION RESULTS

19. RESULTS STATISTICS
Image Hits Repeated False Hits Distance Time (s) Bonus
1 21 0 0 11.1 91 2
2 24 0 0 15.6 90 2
3 25 0 0 10.5 97 0
4 24 0 0 11.8 97 1
5 24 0 0 10.7 103 0
6 24 0 0 9.6 94 0
7 22 0 0 11.2 88 1
Average 23.4 0 0 11.5 94 0.86

21. • CONCLUSION:
• The human face and age is recognized
with 99% Accurately. Successfully found easy and
fast way to detect age and gender of not only a
single person and also group of members.

22. THANKYOU