1 / 22

Introduction to Computational and Biological Vision Liron Michaely Alon Grubshtein

The Face Recognition Problem Is a single image database sufficient for face recognition? A Psychological Experiment. Introduction to Computational and Biological Vision Liron Michaely Alon Grubshtein. Introduction.

callum-sampson
Download Presentation

Introduction to Computational and Biological Vision Liron Michaely Alon Grubshtein

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Face Recognition ProblemIs a single image database sufficient for face recognition?A Psychological Experiment Introduction to Computational and Biological Vision Liron Michaely Alon Grubshtein

  2. Introduction • In recent years facial recognition has become a very popular area of research in computer vision. • Progress in the field of computer vision may require deep physiological understanding, and in turn may advance certain theories in the field of neuroscience. The Face Recognition Problem: Given an image of a scene, identify or verify the person in the scene using a stored database of faces.

  3. Introduction (contd.) • There are different approaches to understanding the way we see things, which in turn inspire different algorithms. • Global Approach - a single feature vector that represents the whole face image is used as input to a classifier. Works well for classifying frontal views of faces. Corresponds with the psychological theory known as the Template Theory. • Component-Based approach – This approach classifies local facial component. Corresponds with the psychological Feature theory.

  4. Introduction (contd.) • Since both solutions are based on comparisons, efficiency is based on both the time required for computation but also on the size of the database required. • In our experiment we want to examine the minimum size of database required for successful face recognition

  5. Our assumption: The human visual system is capable of successful facial recognition based on a single image, even under extreme conditions.

  6. Experimental Procedure • The experiment we conducted was based upon an application we wrote . • Subjects were exposed to a single frontal image of a face (the target), and were tested on their ability to distinguish the target from different other faces (the distracters). • Our target was a young man, Chen Michaely. As distracters we used a group of eight young men with more or less similar prominent facial features

  7. Experiment (contd.) • In the first stage of the experiment we presented the target to our test subjects a written description of him (his habits, details about his lifestyle, etc’…), and then his 0 degree image. • Second stage was a series of seemingly random pictures of both target and distracters from different angles. Subjects were asked to press a ‘Yes’ button upon identifying the target. • Response time was also kept.

  8. Experiment (contd.) • Implemented as a C++ program. • Results saved as a CSV file.

  9. Experiment (contd.) Consistency precautions: • Angle accuracy • Increased Similarity • Control image • Image Order

  10. Results • Several definitions: • A correct “yes” answer is called a “Hit” (H). • A correct “no” answer is called a “Correct Rejection” (CR). • A wrong “yes” answer is called a “False Alarm” (FA). • A wrong “no” answer is called a “Miss” (M). • Results from 40 people were analyzed.

  11. Results (contd.)

  12. Results (contd.) • No wrong answers in Control Image. • Most FA mistakes occurred in two specific images of the same distracter.

  13. Results (contd.)

  14. Results (contd.)

  15. Results (contd.)

  16. Results (contd.)

  17. Results (contd.) • If we consider mistakes and response time as indicators to difficult images, then the direction of face does not necessarily predict the difficulty level of the required analysis.

  18. Conclusions The HVS is capable of processing an image, in such a way that allows it to identify a face from a new point of view, not previously in its database, and relying on a single frontal image.

  19. Discussion • Main question: how is this done? • A Computer Science approach: Create a 3D morphable model from three pictures. Use model to calculate new synthetic images, and compare using component based algorithm. • 90% accuracy. • Is this also what we do? • non existing relation between the performance level and the angle of the face. • The HVS produces significantly lower results in the case of upside down faces.

  20. Bibliography • [1]. Bernd Heisele, Purdy Ho, Jane Wu, and Tomaso Poggio (2003) “Face recognition: component-based versus global approaches”. Computer Vision and Image Understanding 91 (2003) 6–21 • [2]. Karl Haberlandt.” Cognitive Psychology”, 2nd –Ed, Trinity college. • [3]. Jennifer Huang, Bernd Heisele, and Volker Blanz (2003). “Component-based Face Recognition with 3D Morphable Models”. • [4]. Yin, R.K. (1969) “Looking at upside-down faces”. J. Exp. Psychology 81, 141–145 • [5]. Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, Richard Russell. “Face Recognition by Humans: 20 Results all Computer Vision Researchers Should Know About”.

  21. Acknowledgment

More Related