Implementasi Convolutional Neural Network untuk Facial Recognition

Setiawan, Dhanny; Putra, Andikha Dwi; Stefani, Kezia; Felisa, Jenisa

doi:10.37595/mediainfo.v20i2.68

Cited by 2 publications

(2 citation statements)

References 7 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[6]. Research [7] shows that CNN can perform vehicle color recognition by 73.00% for training and 76.94% for image data testing. These results indicate that the model can recognize the color of the vehicle quite well.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Colorization of Black and White Photos

Masruroh

Fiade

Tanggok

et al. 2023

matrik

View full text Add to dashboard Cite

People today are very fond of capturing moments by taking pictures. Various photo functions are used to document all forms of information that you want to store. In photos with digital images that have black and white, the information obtained is less than optimal, so an image processing process is needed to get color photos. Based on this, the author wants to change photos from black and white to color photos. The method used in this research is Convolutional Neural Network (CNN). This study uses Atlas 200 DK hardware and Ascend 310 processor. The data used in this study are 32 black and white photos in .jpg format as training data and perform 6 experimental scenarios with different numbers of black and white photos in each experiment. The total black and white photos used to experiment were 81 photos. The results obtained are models that successfully perform processing in the form of color photos with the appropriate color results in predicting the possible color of the object in each pixel in the photo. Based on this research, the trend of artificial intelligence can be implemented in changing the color of photos according to color predictions.

show abstract

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Colorization of Black and White Photos

Masruroh

Fiade

Tanggok

et al. 2023

matrik

View full text Add to dashboard Cite

show abstract

“…The result of this application is face detection using Haar Cascade with the help of the OpenCV library. The number of face images used in this study was 90,000 from 36 sets of images, producing an accuracy rate of 97% [16]. The other journal makes face recognition applications using the Haar cascade method to obtain optimum accuracy for analyzing faces.…”

Section: Introductionmentioning

confidence: 99%

Real-time Face Recognition System Using Deep Learning Method

Wirdiani,

Darma Putra,

Sudarma

et al. 2023

LKJITI

View full text Add to dashboard Cite

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.

show abstract

Implementasi Convolutional Neural Network untuk Facial Recognition

Cited by 2 publications

References 7 publications

Convolutional Neural Network for Colorization of Black and White Photos

Convolutional Neural Network for Colorization of Black and White Photos

Real-time Face Recognition System Using Deep Learning Method

Contact Info

Product

Resources

About