About driver awareness

1. Real Time Driver Drowsiness Detection
Using Haar Cascade Algorithm
Presented by
SANDHIYA V
SARANYA C
SARANYA S
Under the guidance of
Mr. Murudass, MSC. M.Phil., MCA.,

2. ABSTRACT
 To check the driver fatigue by detecting the facial expressions of
the drivers.
 Using face detection technique.
 On the Dlib toolkit, the lankmark of frontal driver facial emotion
in a frame are found.
 By taking the eyes aspect ratio as input.
 If the drivers drowsiness detection it generates voice alert to
make the driver awake .

3. System specification
• Hardware Specification
• Processor : INTEL I5 (7th generation)
• RAM : 4 GB RAM
• Hard disk : 1TB
• Monitor : 20’ color monitor
•
• Software Specification
• Front end : PyQt5 designer
• Back end : python
• Software tool used : Visual code studio
• Platform : Windows 8

4. EXISTING SYSTEM
 In pre-existing features for facial landmark detection
 It is implemented to identify the state of drowsiness and fatigue.
 68- facial landmark predefined landmark helps in shape prediction
 It identify the various regions of the face like
 Eye brows,
 Eye,
 Mouth region,
 etc.,

5. DISADVANTAGES
 The algorithm used here, rely on the detected facial landmarks.
 It make the system less sensitive to landmark misalignment.
 It Cannot detect exact drowsiness.

6. PROPOSED SYSTEM
 It is being developed is focused on image recognition based on drowsiness.
 Drowsiness can be detected by scrutinizing the status of eye.
 This algorithm which will automatically extract eye area.
 And Scrutinize this eye part for drowsiness detection based on status of
eye.
 It is framework is done on the based of real time face.
 It gives a voice alert to driver when drowsiness is detected.

7. ADVANTAGES
 It detecting the drowsiness of the driver,
 The system also alerts the driver to make him awake at the right time.
 It achieves high precision in detection of drowsiness.

8. MODULES
There are five modules in this system:
• Real-time camera monitoring module.
• Face region detection module.
• Eyes part scrutinization module .
• Drowsiness detection module.
• Voice alert module.

9. REAL TIME CAMERA MONITORING
 The real-time camera is used to get input image.
 As the camera will capture live video streaming,
 Convert the video into image frames
 By extracting the images from video.

10. FACE REGION DETECTION
 It used HAAR cascade classifier to detect.
 It detect face region in the images.

11. EYES PART SCRUTINIZATION
 The Dlib toolkit is used to extract prime facial features.
 It present in the face region of the image.
 Facial landmark algorithm will efficiently locate the region of interest
 For Exemple
• Eye Region

12. DROWSINESS DETECTION
 The drowsiness of the driver will be detected.
 By determining the eyes aspect ratio.
 Eyes aspect ratio is calculated.
 By estimating the changes in that parameter’s value.
 It can determine the drowsy state of the driver.

13. VOICE ALERT
 The driver will be warned by generating voice alert
 The driver is detected to be in drowsy state
 To avoid those serious consequences,
 The system alerts the driver to make him attentive right after the detection
of drowsiness.

14. Conclusion
 HAAR cascade classifier and facial landmark algorithm is used to detect
many faces and eyes, nose, mouth etc.
 It detection of features increases rapidly when a camera of high
specifications is used.
 We are implementing this system in vehicles,
 It only the driver’s facial expression.
 The face is detected and recognized as drowsy,
 It will get an voice alert to driver.

15. Thank You