The document describes an intelligence security system for women that was developed for a hackathon. The system uses a bi-camera setup to detect threats and alert authorities. Camera 1 detects the number of people and triggers Camera 2 if more than one is detected. Camera 2 performs gender and facial expression recognition, and will sound an alarm if a female shows fear, disgust or sadness. The location is then sent to nearby police stations and women's help centers via SMS and email. MATLAB is used to simulate facial expression recognition using eigenfaces. An AngularJS user interface displays the detected location on a map. The system was built using Spring framework, J2EE, REST APIs and MySQL database.

1. An Intelligence Security System
for Women
-K M Aishwarya
-S R Prathiksha
-Shubhangi Agarwal

2. Why this?
In the month of September, as a part of Hike India’s and
Dexter Talent Lab’s collaborative & innovative approach
to increase women safety in our society, we participated
in a Hackathon organized for women that was aimed at
building a software product that helps increase safety for
women in India.
Being fortunate enough to secure the 3rd position in the
hackathon, we got a chance to visit the Hike Messenger’s
HQ in New Delhi, India; and present our protocol to the
CEO & Founder of Hike: Kavin Bharti Mittal.

3. Overview
Enclosed within these slides is a brief description of:
• The problem statement we tried to solve
• Our approach towards it
• The solution we came up with.

4. Problem Statement
Build a security system for women that is
completely automated and requires no human
interference whatsoever.

5. Approach
The system should be fool proof and
independent of any mobile or web application.
So that safety is ensured even if the victim is not
in possession of a phone at the time of the
crime.

6. Solution
• We built a MATLAB-web interface wherein:
– An arrangement of a bi-camera setup (2 cameras)
– One placed at a higher altitude than the other.
– Camera 1: Captures image. Detects the number of people in the image.
– If more than 1 person detected, signal sent to Camera 2.
– Camera 2: Captures image. Gender recognition performed based on facial
features.
– If even one female is recognized, Facial Expression Recognition is
performed.
– On detecting emotions of fear, disgust or sadness, Camera 2 sends off an
alarm to the web server.
– Backend code retrieves latitude and longitude of the camera where threat
is detected.
– Alert SMS’s and emails sent to the nearby police stations by web server.

7. I. MATLAB Simulation
• Version used: R2009a
• Implemented for: Facial Expression
Recognition (Eigen Face Recognition Method)

8. Why use MATLAB?
– In built toolboxes: Image Processing toolbox,
Artificial Neural Networks toolbox, audio-video
processing toolbox
– Pre-processing is way easier!
– A vast base for simulation of both hardware and
software components.
– Incorporates Linear Algebra for all mathematical
calculations.

9. Mood Detection
• The 7 basic moods detected on human face in
MATLAB:
– Happiness
– Sadness
– Fear
– Disgust
– Angry
– Surprise
– Neutral

10. Steps
• Capture an image
• Pre-processing of input image
• Perform PCA (Principal Component Analysis),
identify and concentrate on the skin-area of the
face.
• Perform Facial Expression Recognition using Eigen
Face recognition.
• Display output image with the expression label of
the matched image from the train database.

11. Step 1 - Preprocessing
– Denoising the image (removes unwanted
distortions that are seen as noise)
– Feature extraction (focuses on facial features)
– Cropping (background is cropped)
– Recognizing and focusing on the face area

12. Step 2 - PCA
Few instances of usage of PCA:
– Dimensionality reduction (used in this case)
– Pattern Recognition
– Data Reduction (audio/video/image etc.)
PCA is a vast topic in itself, which cannot be covered within these slides
alone. Hence only its areas of application have been mentioned here.

13. Step 3 - Facial Expression Recognition
• Given a prior database of images with each one
of them labeled with a facial expression, an input
captured image has to be pre-processed,
compressed and matched with one of the images
in the training set.
• Once recognized, the label assigned to the image
identified from the training set is assigned to
input image.
• Hence the facial expression of the captured
image is displayed, based on which an alarm is
set off.

14. II. Developing the User
Interface

15. UI – AngularJS
• Framework for dynamic web apps.
• Mainly used for single page applications.
• A product of Google©.
• Features –
– MVVM(Model-View-ViewModel)
– 2 Way Data Binding
– Filters and dynamic templating.

16. Working
• Backend code sends the latitude and longitude
of the place where a threat gets detected.
• Reverse geocoding the latitude and longitude
attributes and displaying of the location on
Google maps, by creating markers.
• Thus, location is sent to helping centers and
police stations nearby.

17. Why AngularJS?
• Dynamically fetches results and updates view.
• Pairs with AJAX for amazing speeds.
• Can be easily incorporated with iOs and
Android.

18. Emergency Alert Service (EAS)
 Instant email alerts sent to all the Durga Centers (women help
centers), police stations and the volunteers nearby.
 Provision for voluntary signup as volunteers on the website made
available.
 Send Text Messages, Hike messages to volunteers according to
their preferences.
 Finding out the availability of volunteers as to whether they are in
town or not.
 In cases of the volunteer being out of reach, the message alerts
can be disabled.
 Provision of extra services (like automated calls) parallely with the
message notifications.

19. III. Tech Stack
Spring framework.
J2EE
REST APIs
MySQL
• Why APIs?
For reusability purposes.
• Why J2EE and Spring?
Tends to impose as little constraints as possible.
• Why MySQL?
Faster and more reliable.

20. Thank you!
We hope you go ahead and contribute and make
this world a better place to be in! 

21. Any questions?
Drop a mail to: aish3095@gmail.com