3D and 2D face recognition based on image segmentation

Belahcene, Mébarka; Chouchane, Ammar; Benatia, Mohamed Amin; Halitim, M.

doi:10.1109/iwcim.2014.7008800

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Besides, in reality, a human face is made of a 3D model; it's more realistic to segment and recognize images based on 3D faces. [45] has proposed a system of face recognition 3D based on segmentation by classifying fields of facial images before and after fusion of color and depth images. It brings us a potential research in face recognition study that we could investigate more about 3D image segmentation technologies instead of 2D.…”

Section: Discussionmentioning

confidence: 99%

Effective Segmented Face Recognition (SFR) for IoT

Gao¹,

Liu²

2020

Adv. sci. technol. eng. syst. j.

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Section: Discussionmentioning

confidence: 99%

Effective Segmented Face Recognition (SFR) for IoT

Gao¹,

Liu²

2020

Adv. sci. technol. eng. syst. j.

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“…Kemudian dekade berikutnya Belahcene, M dkk. [14] mengusulkan sebuah kerangka kerja untuk sistem pengenalan wajah 3D menggunakan segmentasi dengan pengelompokan wilayah gambar wajah sebelum dan sesudah penggabungan dari data gambar dan kedalaman dengan metode klasifikasi menggunakan Support Vector Mechine(SVM). Yu X dkk.…”

Section: A Pendahuluanunclassified

Pengenalan Wajah 3D dengan menggunakan PointNet

Arif Hidayah,

Dewantara,

Pramadihanto

2023

ijcs

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Pengenalan wajah tiga dimensi (3D) telah menjadi topik penelitian yang menarik karena mampu mengatasi keterbatasan pengenalan wajah dua dimensi (2D) dalam menghadapi perubahan pose, pencahayaan, dan pemalsuan. Penelitian ini mengusulkan sebuah pipeline pengenalan wajah 3D yang invarian terhadap perubahan cahaya, dengan menggunakan teknik segmentasi euclidean clustering dan Convolutional Neural Network (CNN) PointNet. Data wajah diambil menggunakan kamera Time-of-Flight yang menghasilkan titik awan (point cloud). Proses segmentasi euclidean clustering berhasil memisahkan area wajah dengan akurat, membantu dalam pengenalan wajah 3D. Melalui pelatihan dengan 217 dataset dan 2048 titik per wajah, sistem mencapai akurasi pelatihan sebesar 99% dan akurasi validasi sebesar 84,4%, dengan loss pelatihan sebesar 1% dan loss validasi sebesar 15,6%. Evaluasi pada tiap kelas menunjukkan rata-rata akurasi 0.9887471867966992, presisi 0.8255813953488372, recall 0.8255813953488372, dan F1-score 0.8255813953488372. Hasil menunjukkan bahwa pipeline pengenalan wajah 3D ini memiliki potensi besar dalam aplikasi keamanan, pengawasan, dan pengenalan objek di lingkungan yang kompleks. Three-dimensional (3D) face recognition has emerged as an intriguing research topic, addressing the limitations of two-dimensional (2D) face recognition in handling pose variations, lighting changes, and spoofing. This study proposes an illumination-invariant pipeline for 3D face recognition, utilizing the euclidean clustering segmentation technique and Convolutional Neural Network (CNN) PointNet. Facial data is captured using a Time-of-Flight camera, generating point clouds. The euclidean clustering segmentation effectively isolates facial regions, aiding in 3D face recognition. After training with 217 datasets and 2048 points per face, the system achieved 99% training accuracy and 84.4% validation accuracy, with 1% training loss and 15.6% validation loss. Class-wise evaluation yielded an average accuracy of 0.9887471867966992, precision of 0.8255813953488372, recall of 0.8255813953488372, and F1-score of 0.8255813953488372. The results highlight the significant potential of this 3D face recognition pipeline in security, surveillance, and object recognition in complex environments.

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“…Feature vectors of regions are created based on their properties and are used to distinguish them. Image segmentation describes details of the various components in image to classify and recognize objects easier (Nock and Nielsen, 2004;Starck et al, 2005;Yu et al, 2009;Belahcene et al, 2014;Dhanachandra et al, 2015;Minaee and Wang, 2019). For instance, based on image segmentation, face detection contributes to better face recognition and user identification.…”

Section: Introductionmentioning

confidence: 99%

A proposed scenario to improve the Ncut algorithm in segmentation

Tran

Hieu²,

Bao³

2023

Front. Big Data

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In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.

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3D and 2D face recognition based on image segmentation

Cited by 4 publications

References 6 publications

Effective Segmented Face Recognition (SFR) for IoT

Effective Segmented Face Recognition (SFR) for IoT

Pengenalan Wajah 3D dengan menggunakan PointNet

A proposed scenario to improve the Ncut algorithm in segmentation

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