Integrating the original face images and “symmetrical faces” to perform face recognition

Amin

Hartati

et al. 2020

IJEEI

This study proposes a face recognition model using a combination of shape and texture vectors that are used to produce new face images on 2D-3D reconstruction images. The reconstruction process to produce 3D face images is carried out using the convolutional neural network (CNN) method on 2D face images. Merging shapes and textures vector is used to produce correlation points on new face images that have similarities to the initial image used. Principal Component Analysis (PCA) is used as a feature extraction method, and for the classification method, we use the Mahalanobis method. The results of the tests can produce a better recognition rate compared to face recognition testing using 2D images.

Section: Introductionmentioning

confidence: 99%

Face Recognition based on CNN 2D-3D Reconstruction using Shape and Texture Vectors Combining

Amin

Hartati

et al. 2020

IJEEI

“…This study also uses the original image mirroring technique and integrates with the original 2D image of a single vision retrieval technique for use in face recognition. [8] has been conducted the development research of face recognition research that has been done by [6]. The study also utilizes facial symmetry patterns for use in face recognition techniques.…”

Section: Introductionmentioning

confidence: 99%

Anti-Cheating Presence System Based on 3WPCA- Dual Vision Face Recognition

Hadikurniawati

Amin

et al. 2017

EECSI

To prevent counterfeit face image on face presence system, we can use dual vision camera in face recognition system. Dual vision camera is used to produce detectable face images from two positions of the left lens and the right lens. Image retrieval at the two corners of the left lens and the right lens can produce a merged face image database of left lens face image and right lens face image. The use of two sides of the face angle taking is used to avoid falsification of facial data such as the use of a face photo of a person or an image similar to a person's face. This research uses a dual-vision face recognition method on its preprocessing and uses 3WPCA (Three Level Wavelet Decomposition-Principal Component Analysis) as its feature extraction model. In dual-vision face recognition, we use half-join method to combine a half of the left image and a half of the right image into an image that is ready to be extracted using 3WPCA. This research can produce a presence system based on good face recognition and can be used to anticipate falsification of face data with recognition accuracy up to 98%.

“…A face recognition system that was conducted by [8] was further developed by [11] based on the pattern of half-face image. The difference between the research conducted by [11] and that conducted by [8] was the addition of the training process on the left half of the face image and the right half of the face image.…”

Section: Introductionmentioning

confidence: 99%

“…The difference between the research conducted by [11] and that conducted by [8] was the addition of the training process on the left half of the face image and the right half of the face image.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

Harjoko

Arymurthy

et al. 2016

IJECE

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86%-97% recognition rate on a variety of poses and variations in angles between 0°-22.5°. The variation of illuminance measured using a lux meter can result in 90%-100% recognition rate for the category of at least dim lighting levels (above 10 lux).