5. Определение набора тестов для проверки качества работы системы распознавания

Тестирование системы распознавания целесообразно разбить на тестирование отдельных ее подсистем. Таким образом, получается следующие подзадачи тестирования:

• тестирование подсистемы удаления фона;

• тестирование подсистемы обнаружения лиц;

• тестирование подсистемы распознавания лиц;

  • тестирование модуля выделения признаков;

  • тестирование классификатора.

5.1. Тестирование подсистемы удаления фона

Подсистема удаления фона должна быстро и четко реагировать на событие - появление нового объекта в поле зрения камеры. На основании этого необходимо и производить ее тестирование. Соответственно, достаточно создать ряд тестовых видеопоследовательностей, в которых происходит событие и на этих последовательностях производить проверку подсистемы удаления фона.

5.2. Тестирование подсистемы обнаружения лиц

Для тестирования подсистемы обнаружения лиц необходимо ввести некоторые показатели, характеризующие качество решения задачи обнаружения.

Pпо - вероятность правильного обнаружения.

Pло - вероятность ложного обнаружения.

P12 -вероятность ошибки первого рода (не обнаружили лицо, а оно есть на самом деле)

P21 - вероятность ошибки второго рода (обнаружили лицо, а его на самом деле нет).

Все эти характеристики могут быть вычислены как для задачи обнаружения одного лица, так и для задачи обнаружения нескольких лиц.

Общее тестирование подсистемы обнаружения проводится с возрастанием сложности в несколько этапов:

1) обнаружение лиц на фотографиях (подсистеме подаются отдельные изображения лиц);

2) обнаружение в видеопоследовательностях (подсистеме подается на вход заранее записанная видеопоследовательность, например, avi-файл);

3) обнаружение в реальном времени (на входе подсистемы стоит изображение с веб-камеры).

5.3. Тестирование подсистемы распознавания лиц

Подсистема распознавания лиц является наиболее вычислительно сложной, поэтому для нее задача тестирования должна выполняться более тщательно. Вначале используемые методы выделения признаков и классификации должны быть протестированы отдельно на предмет правильной реализации алгоритмов, заложенных в них. Затем должна быть протестирована вся система распознавания на предмет решения собственно задачи распознавания. В качестве тестовой выборки здесь лучше всего использовать существующие базы данных лиц, специально созданные для. Приведемнекоторыеизних.

5.4. Face Databases

The Color FERET Database, USA

The FERET program set out to establish a large database of facial images that was gathered independently from the algorithm developers. To maintain a degree of consistency throughout the database, the same physical setup was used in each photography session. Because the equipment had to be reassembled for each session, there was some minor variation in images collected on different dates.

The Yale Face Database

Contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.

The Yale Face Database B

Contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). For every subject in a particular pose, an image with ambient (background) illumination was also captured.

PIE Database, CMU

A database of 41,368 images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions.

Project - Face In Action (FIA) Face Video Database, AMP, CMU

Capturing scenario mimics the real world applications, for example, when a person is going through the airport check-in point. Six cameras capture human faces from three different angles. Three out of the six cameras have smaller focus length, and the other three have larger focus length. Plan to capture 200 subjects in 3 sessions in different time period.

AT&T "The Database of Faces" (formerly "The ORL Database of Faces")

Ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).

MIT-CBCL Face Recognition Database

Image Database of Facial Actions and Expressions - Expression Image Database

24 subjects are represented in this database, yielding between about 6 to 18 examples of the 150 different requested actions. Thus, about 7,000 color images are included in the database, and each has a matching gray scale image used in the neural network analysis.

Face Recognition Data, University of Essex, UK

395 individuals (male and female), 20 images per individual. Contains images of people of various racial origins, mainly of first year undergraduate students, so the majority of indivuals are between 18-20 years old but some older individuals are also present. Some individuals are wearing glasses and beards.

NIST Mugshot Identification Database

There are images of 1573 individuals (cases) 1495 male and 78 female. The database contains both front and side (profile) views when available. Separating front views and profiles, there are 131 cases with two or more front views and 1418 with only one front view. Profiles have 89 cases with two or more profiles and 1268 with only one profile. Cases with both fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more fronts and one profile, and 1217 with only one front and one profile.

NLPR Face Database

450 face images. 896 x 592 pixels. JPEG format. 27 or so unique people under with different lighting/expressions/backgrounds.

M2VTS Multimodal Face Database (Release 1.00)

Database is made up from 37 different faces and provides 5 shots for each person. These shots were taken at one week intervals or when drastic face changes occurred in the meantime. During each shot, people have been asked to count from '0' to '9' in their native language (most of the people are French speaking), rotate the head from 0 to -90 degrees, again to 0, then to +90 and back to 0 degrees. Also, they have been asked to rotate the head once again without glasses if they wear any.

The AR Face Database, Purdue University, USA

4,000 color images corresponding to 126 people's faces (70 men and 56 women). Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions (sun glasses and scarf).

The University of Oulu Physics-Based Face Database

Contains 125 different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses. Faces in frontal position captured under Horizon, Incandescent, Fluorescent and Daylight illuminant .Includes 3 spectral reflectance of skin per person measured from both cheeks and forehead. Contains RGB spectral response of camera used and spectral power distribution of illuminants.

Japanese Female Facial Expression (JAFFE) Database

The database contains 213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models. Each image has been rated on 6 emotion adjectives by 60 Japanese subjects.

BioID Face DB - HumanScan AG, Switzerland

The dataset consists of 1521 gray level images with a resolution of 384x286 pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions.

The Sheffield Face Database (previously: The UMIST Face Database)

Consists of 564 images of 20 people. Each covering a range of poses from profile to frontal views. Subjects cover a range of race/sex/appearance. Each subject exists in their own directory labelled 1a, 1b, ... 1t and images are numbered consequetively as they were taken. The files are all in PGM format, approximately 220 x 220 pixels in 256 shades of grey.

Caltech Faces

450 face images. 896 x 592 pixels. JPEG format. 27 or so unique people under with different lighting/expressions/backgrounds.

etc…

Большое количество баз с лицами дает определенную свободу в выборе и позволяет исследовать подсистему распознавания лиц на самые различные типы инвариантностей (к освещению, повороту, деталям лица и др.).