Identification system from motion pictures: LBPH application

Haşıloğlu, Abdulsamet; Olcay, Tosun

doi:10.1109/ubmk.2017.8093546

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their study, Abdülsamed and Olcay observed that, using the AT&T database in facial recognition, successful results were achieved. However while they used the actor images in the movies, an average success was achieved [6]. They attributed the high recognition rate in the ready database to the constant positioning and lighting environment.…”

Section: Introductionmentioning

confidence: 99%

“…They attributed the high recognition rate in the ready database to the constant positioning and lighting environment. On the other hand, images captured from daily life obtains low rate recognition rates because of reasons like very different light levels, different perspectives and applying different makeup to people at different times ( [6], [7]). In Aksoy's study [8], front, side, upper and lower face images were taken from volunteer students and faculty members.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification with Face Recognition Methods in Real Life Applications

Ediz¹

2021

International Journal of Engineering Technologies IJET

View full text Add to dashboard Cite

The development of technologies such as employment tracking systems, personal security, and the use of robots has led a lot of studies on face recognition systems. In the most of studies considering face recognition, recognition accuracies are very high, since training and testing images are selected randomly from the same databases. However, in real life applications, these images are not randomly selected from the same database. Because, these systems are trained during installation of the recognition system or when a new person needs to be introduced to the system. On the other hand, images used for predictions are uploaded to the system at other times. In this study, it is aimed to show that the accuracy rates of real-life face recognition systems differ from the systems trained and tested with randomly selected images as usually done in literature. To observe this difference in the first step, training and test images are selected randomly. In the second step, training and test images are divided according to the recording dates as in real life. Accuracy rates are evaluated by using linear discriminant analysis, local binary patterns and principal component analysis methods. Although the accuracies are very high for the first step, it is seen that the accuracies fell dramatically in the second step for all methods. Afterwards a new method is searched also in this study to increase these low accuracy rates. It is shown that usage of eye area images instead of face images has higher accuracy rates in all above methods for real life applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%