Figure 2: Some Applications of Artificial Intelligence


Practical Application of deep learning - Facial Recognition



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Practical Application of deep learning - Facial Recognition 
(Contd)
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Figure 19: Face Recognition Using deep networks (Source: www.edureka.co)


Deep Face Recognition
(1) Phase-I: Enrollment 
phase – Model / system is 
trained using millions of 
prototype face images and 
a trained model is 
generated. Generated face 
features are stored in 
database and 
(2) Phase-II: Recognition 
phase – Query face image 
is given as input to the 
model generated in phase-
I to recognise it correctly. 
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Face recognition applications have two parts or phases viz:
Figure 20: Face Recognition Architecture (Source: aiehive.com)


Deep Face Recognition (Contd)
Steps within Enrollment 
Phase Includes
1. Face Detection
2. Feature extraction
3. Store Model and extracted 
feature in Database
Steps within Recognition Phase / 
Query Phase Includes
1. Face Detection
2. Preprocessing
3. Feature Extraction
4. Recognition
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Step 1: Face Detection -
Enrollment Phase 
Face Detection: Face needs to be 
located and region of interest is 
computed.

Histogram of Oriented 
Gradients (HOG) is a faster 
and easier algorithm for face 
detection. 

Detected faces are given to 
next step of feature extraction.
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Figure 21: Multiple Face Detection (Source: aiehive.com)


Step 2: Feature Extraction-

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