Face recognition - Revision history

Onnowpurbo: /* Thermal cameras */

2018-05-24T09:18:29Z

Thermal cameras

← Older revision		Revision as of 09:18, 24 May 2018
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	Teknik gabungan memiliki keunggulan dibandingkan sistem lain. Ini relatif tidak sensitif terhadap perubahan ekspresi, termasuk berkedip, mengerutkan dahi atau tersenyum dan memiliki kemampuan untuk mengkompensasi kumis atau pertumbuhan janggut dan penampilan kacamata. Sistem ini juga seragam sehubungan dengan ras dan gender.		Teknik gabungan memiliki keunggulan dibandingkan sistem lain. Ini relatif tidak sensitif terhadap perubahan ekspresi, termasuk berkedip, mengerutkan dahi atau tersenyum dan memiliki kemampuan untuk mengkompensasi kumis atau pertumbuhan janggut dan penampilan kacamata. Sistem ini juga seragam sehubungan dengan ras dan gender.

	===~~Thermal cameras~~===		===Kamera Panas===

	~~A different form of taking~~ input ~~data for face recognition is by using thermal cameras~~, ~~by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses~~, ~~hats~~, or make up. ~~A problem with using thermal pictures for face recognition is that the databases for face recognition is limited~~. Diego Socolinsky, ~~and~~ Andrea Selinger (2004) ~~research the use of thermal face recognition in real life~~, ~~and operation sceneries~~, ~~and at the same time build a new~~ database ~~of thermal face images~~. ~~The research uses~~ low-sensitive, low-resolution ferro-electric electrics sensors ~~that are capable of acquire~~ long wave thermal infrared (LWIR). ~~The results show that a fusion of~~ LWIR ~~and regular~~ visual ~~cameras has the greater results in outdoor probes~~. ~~Indoor results show that~~ visual ~~has a~~ 97.05% ~~accuracy~~, ~~while~~ LWIR ~~has~~ 93.93%, ~~and the~~ Fusion ~~has~~ 98.40%, ~~however on the outdoor proves~~ visual ~~has~~ 67.06%, LWIR 83.03%, ~~and fusion has~~ 89.02%. ~~The study used~~ 240 ~~subjects over the period of~~ 10 ~~weeks to create the new~~ database. ~~The data was collected on sunny~~, ~~rainy~~, ~~and cloudy days~~.		Bentuk berbeda dari pengambilan data input untuk pengenalan wajah adalah dengan menggunakan kamera termal, dengan prosedur ini kamera hanya akan mendeteksi bentuk kepala dan akan mengabaikan aksesori subjek seperti kacamata, topi, atau make up. Masalah dengan menggunakan gambar termal untuk pengenalan wajah adalah bahwa database untuk pengenalan wajah terbatas. Diego Socolinsky, dan Andrea Selinger (2004) meneliti penggunaan pengenalan wajah termal dalam kehidupan nyata, dan pemandangan operasi, dan pada saat yang sama membangun database baru dari citra wajah termal. Penelitian ini menggunakan low-sensitive, low-resolution ferro-electric electrics sensors dan beresolusi rendah yang mampu memperoleh long wave thermal infrared (LWIR). Hasilnya menunjukkan bahwa peleburan LWIR dan kamera visual biasa memiliki hasil yang lebih besar dalam pemeriksaan luar ruangan. Hasil dalam ruangan menunjukkan bahwa visual memiliki akurasi 97,05%, sementara LWIR memiliki 93,93%, dan Fusion memiliki 98,40%, namun di luar ruangan membuktikan visual memiliki 67,06%, LWIR 83,03%, dan fusi memiliki 89,02%. Penelitian ini menggunakan 240 subjek selama periode 10 minggu untuk membuat database baru. Data dikumpulkan pada hari-hari cerah, hujan, dan berawan.

	==Pranala Menarik==		==Pranala Menarik==

Onnowpurbo: /* Facial recognition combining different techniques */

2018-05-24T09:15:57Z

Facial recognition combining different techniques

← Older revision		Revision as of 09:15, 24 May 2018
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	Beberapa tes menunjukkan bahwa dengan penambahan analisis tekstur kulit, kinerja dalam pengenalan wajah dapat meningkat 20 hingga 25 persen.		Beberapa tes menunjukkan bahwa dengan penambahan analisis tekstur kulit, kinerja dalam pengenalan wajah dapat meningkat 20 hingga 25 persen.

	===~~Facial recognition combining different techniques~~===		===Pengenalan Wajah menggabungkan beberapa teknik===

	As every methods has its advantages and disadvantages, technology companies have amalgamated the traditional, 3D recognition and Skin Textual Analysis, to create recognition systems that have higher rates of success.

	Combined techniques has an advantage over other systems. It is relatively insensitive to changes in expression, including blinking, frowning or smiling and has the ability to compensate for mustache or beard growth and the appearance of eyeglasses. The system is also uniform with respect to race and gender.

			Karena setiap metode memiliki kelebihan dan kekurangan, perusahaan teknologi telah menggabungkan pengenalan 3D, tradisional dan Skin Textual Analysis, untuk menciptakan sistem pengenalan yang memiliki tingkat keberhasilan yang lebih tinggi.

			Teknik gabungan memiliki keunggulan dibandingkan sistem lain. Ini relatif tidak sensitif terhadap perubahan ekspresi, termasuk berkedip, mengerutkan dahi atau tersenyum dan memiliki kemampuan untuk mengkompensasi kumis atau pertumbuhan janggut dan penampilan kacamata. Sistem ini juga seragam sehubungan dengan ras dan gender.

	===Thermal cameras===		===Thermal cameras===

Onnowpurbo: /* Analisa Skin texture */

2018-05-24T09:14:16Z

Analisa Skin texture

← Older revision		Revision as of 09:14, 24 May 2018
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	Surface Texture Analysis, bekerja dengan cara yang sama dengan pengenalan wajah. Sebuah gambar diambil dari sepetak kulit, yang disebut skinprint. Tambalan itu kemudian dipecah menjadi balok-balok yang lebih kecil. Menggunakan algoritma untuk mengubah patch menjadi ruang matematis dan terukur, sistem akan membedakan garis, pori dan tekstur kulit yang sebenarnya. Ini dapat mengidentifikasi perbedaan antara kembar identik, yang belum mungkin menggunakan perangkat lunak pengenalan wajah saja.		Surface Texture Analysis, bekerja dengan cara yang sama dengan pengenalan wajah. Sebuah gambar diambil dari sepetak kulit, yang disebut skinprint. Tambalan itu kemudian dipecah menjadi balok-balok yang lebih kecil. Menggunakan algoritma untuk mengubah patch menjadi ruang matematis dan terukur, sistem akan membedakan garis, pori dan tekstur kulit yang sebenarnya. Ini dapat mengidentifikasi perbedaan antara kembar identik, yang belum mungkin menggunakan perangkat lunak pengenalan wajah saja.

	~~Tests have shown that with the addition of skin texture analysis~~, ~~performance in recognizing faces can increase~~ 20 to 25 ~~percent~~.		Beberapa tes menunjukkan bahwa dengan penambahan analisis tekstur kulit, kinerja dalam pengenalan wajah dapat meningkat 20 hingga 25 persen.

	===Facial recognition combining different techniques===		===Facial recognition combining different techniques===

Onnowpurbo: /* Analisa Skin texture */

2018-05-23T06:52:59Z

Analisa Skin texture

← Older revision		Revision as of 06:52, 23 May 2018
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	Tren lain yang muncul menggunakan detail visual kulit, seperti yang ditangkap dalam gambar digital atau pindaian standar. Teknik ini, yang disebut Skin Texture Analysis, mengubah garis, pola, dan bintik unik yang tampak pada kulit seseorang menjadi ruang matematika.		Tren lain yang muncul menggunakan detail visual kulit, seperti yang ditangkap dalam gambar digital atau pindaian standar. Teknik ini, yang disebut Skin Texture Analysis, mengubah garis, pola, dan bintik unik yang tampak pada kulit seseorang menjadi ruang matematika.

	Surface Texture Analysis, ~~works much the same way facial recognition does~~. ~~A picture is taken of a patch of skin~~, ~~called a~~ skinprint. ~~That patch is then broken up into smaller blocks~~. ~~Using algorithms to turn the~~ patch ~~into a mathematical, measurable space~~, ~~the system will then distinguish any lines~~, ~~pores and the actual skin texture~~. ~~It can identify differences between identical twins~~, ~~which is not yet possible using facial recognition software alone~~.		Surface Texture Analysis, bekerja dengan cara yang sama dengan pengenalan wajah. Sebuah gambar diambil dari sepetak kulit, yang disebut skinprint. Tambalan itu kemudian dipecah menjadi balok-balok yang lebih kecil. Menggunakan algoritma untuk mengubah patch menjadi ruang matematis dan terukur, sistem akan membedakan garis, pori dan tekstur kulit yang sebenarnya. Ini dapat mengidentifikasi perbedaan antara kembar identik, yang belum mungkin menggunakan perangkat lunak pengenalan wajah saja.

	Tests have shown that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 percent.		Tests have shown that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 percent.

Onnowpurbo: /* Analisa Skin texture */

2018-05-23T06:51:28Z

Analisa Skin texture

← Older revision		Revision as of 06:51, 23 May 2018
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	===Analisa Skin texture===		===Analisa Skin texture===

	~~Another emerging trend uses the~~ visual ~~details of the skin~~, ~~as captured in standard~~ digital ~~or scanned images~~. ~~This technique~~, ~~called~~ Skin Texture Analysis, ~~turns the unique lines~~, ~~patterns~~, ~~and spots apparent in a person’s skin into a mathematical space~~.		Tren lain yang muncul menggunakan detail visual kulit, seperti yang ditangkap dalam gambar digital atau pindaian standar. Teknik ini, yang disebut Skin Texture Analysis, mengubah garis, pola, dan bintik unik yang tampak pada kulit seseorang menjadi ruang matematika.

	Surface Texture Analysis, works much the same way facial recognition does. A picture is taken of a patch of skin, called a skinprint. That patch is then broken up into smaller blocks. Using algorithms to turn the patch into a mathematical, measurable space, the system will then distinguish any lines, pores and the actual skin texture. It can identify differences between identical twins, which is not yet possible using facial recognition software alone.		Surface Texture Analysis, works much the same way facial recognition does. A picture is taken of a patch of skin, called a skinprint. That patch is then broken up into smaller blocks. Using algorithms to turn the patch into a mathematical, measurable space, the system will then distinguish any lines, pores and the actual skin texture. It can identify differences between identical twins, which is not yet possible using facial recognition software alone.

	Tests have shown that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 percent.		Tests have shown that with the addition of skin texture analysis, performance in recognizing faces can increase 20 to 25 percent.


	===Facial recognition combining different techniques===		===Facial recognition combining different techniques===

Onnowpurbo: /* Beberapa Teknik Pengenalan Wajah */

2018-05-23T06:50:07Z

Beberapa Teknik Pengenalan Wajah

← Older revision		Revision as of 06:50, 23 May 2018
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	===Pengenalan 3-Dimensi===		===Pengenalan 3-Dimensi===

	~~Three-dimensional face recognition technique uses~~ 3D ~~sensors to capture information about the shape of a face~~. ~~This information is then used to identify distinctive features on the surface of a face~~, ~~such as the contour of the eye sockets~~, ~~nose~~, ~~and chin~~.		Teknik pengenalan wajah tiga dimensi menggunakan sensor 3D untuk menangkap informasi tentang bentuk wajah. Informasi ini kemudian digunakan untuk mengidentifikasi ciri khas pada permukaan wajah, seperti kontur mata, hidung, dan dagu.

	~~One advantage of~~ 3D ~~face recognition is that it is not affected by changes in lighting like other techniques~~. ~~It can also identify a face from a range of viewing angles~~, ~~including a profile view~~. ~~Three-dimensional~~ data ~~points from a face vastly improve the precision of face recognition~~. 3D ~~research is enhanced by the development of sophisticated sensors that do a better job of capturing~~ 3D ~~face imagery~~. ~~The sensors work by projecting structured light onto the face~~. ~~Up to a dozen or more of these image sensors can be placed on the same~~ CMOS ~~chip—each~~ sensor ~~captures a different part of the spectrum~~....		Satu keuntungan dari pengenalan wajah 3D adalah bahwa dia tidak dipengaruhi oleh perubahan pencahayaan seperti teknik lainnya. Ini juga dapat mengidentifikasi wajah dari berbagai sudut pandang, termasuk tampilan profil. Titik data tiga dimensi dari wajah sangat meningkatkan ketepatan pengenalan wajah. Riset 3D ditingkatkan dengan pengembangan sensor canggih yang melakukan pekerjaan lebih baik dalam menangkap citra wajah 3D. Sensor bekerja dengan memproyeksikan cahaya terstruktur ke wajah. Hingga selusin atau lebih dari sensor gambar ini dapat ditempatkan pada chip CMOS yang sama — setiap sensor menangkap bagian yang berbeda dari spektrum ....

	~~Even a perfect~~ 3D ~~matching technique could be sensitive to expressions~~. ~~For that goal a~~ group ~~at the~~ Technion ~~applied tools from metric geometry to treat expressions as isometries~~		Bahkan teknik pencocokan 3D yang sempurna bisa menjadi peka terhadap ekspresi. Untuk tujuan itu sebuah group di Technion diterapkan tool dari geometri metrik untuk memperlakukan ekspresi sebagai isometry.

	~~A new method is to introduce a way to capture a~~ 3D ~~picture by using three tracking cameras that point at different angles~~; ~~one camera will be pointing at the front of the subject~~, ~~second one to the side~~, ~~and third one at an angle~~. ~~All these cameras will work together so it can track a subject’s face in~~ real time ~~and be able to face detect and recognize~~.		Metode baru adalah memperkenalkan cara untuk menangkap gambar 3D dengan menggunakan tiga kamera pelacak yang menunjuk pada sudut yang berbeda; satu kamera akan menunjuk di depan subjek, yang kedua ke samping, dan yang ketiga pada sudut. Semua kamera ini akan bekerja bersama sehingga dapat melacak wajah subjek secara real-time dan dapat mendeteksi dan mengenali wajah.


	===Skin texture ~~analysis~~===		===Analisa Skin texture===

	Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. This technique, called Skin Texture Analysis, turns the unique lines, patterns, and spots apparent in a person’s skin into a mathematical space.		Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. This technique, called Skin Texture Analysis, turns the unique lines, patterns, and spots apparent in a person’s skin into a mathematical space.

Onnowpurbo: /* Beberapa Teknik Pengenalan Wajah */

2018-05-23T06:32:06Z

Beberapa Teknik Pengenalan Wajah

← Older revision		Revision as of 06:32, 23 May 2018
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	==Beberapa Teknik Pengenalan Wajah==		==Beberapa Teknik Pengenalan Wajah==

	===~~Traditional~~===		===Tradisional===

	~~Some face recognition algorithms identify facial features by extracting landmarks~~, ~~or features~~, ~~from an image of the subject's face~~. ~~For example~~, ~~an algorithm may analyze the relative position~~, ~~size~~, ~~and~~/~~or shape of the eyes~~, ~~nose~~, ~~cheekbones~~, ~~and jaw~~. ~~These features are then used to search for other images with matching features~~.		Beberapa algoritma pengenalan wajah mengidentifikasi fitur wajah dengan mengekstraksi landmark, atau fitur, dari gambar wajah subjek. Sebagai contoh, suatu algoritma dapat menganalisa posisi relatif, ukuran, dan / atau bentuk mata, hidung, tulang pipi, dan rahang. Fitur-fitur ini kemudian digunakan untuk mencari gambar lain dengan fitur yang cocok.

	~~Other algorithms normalize a gallery of face images and then compress the face~~ data, ~~only saving the~~ data ~~in the image that is useful for face recognition~~. ~~A probe image is then compared with the face~~ data. ~~One of the earliest successful systems is based on~~ template ~~matching techniques applied to a~~ set ~~of salient facial features~~, ~~providing a sort of compressed face representation~~.		Algoritma lainnya menormalisasi galeri gambar wajah dan kemudian memampatkan data wajah, hanya menyimpan data dalam gambar yang berguna untuk pengenalan wajah. Citra yang di analisa kemudian dibandingkan dengan data wajah. Salah satu sistem awal yang paling sukses adalah berdasarkan teknik pencocokan template yang diterapkan pada satu set fitur wajah yang menonjol, menyediakan semacam representasi wajah terkompresi.

	~~Recognition algorithms can be divided into two main approaches~~, ~~geometric~~, ~~which looks at distinguishing features~~, ~~or photometric~~, ~~which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances~~.		Algoritma pengenalan dapat dibagi menjadi dua pendekatan utama, geometrik, yang melihat fitur pembeda, atau fotometri, yang merupakan pendekatan statistik yang menyaring gambar ke dalam nilai dan membandingkan nilai dengan template untuk menghilangkan varian.

	~~Popular recognition algorithms include principal component analysis using~~ eigenfaces, linear discriminant analysis, elastic bunch graph matching ~~using the~~ Fisherface algorithm, the hidden Markov model, the multilinear subspace learning using tensor representation, ~~and the~~ neuronal motivated dynamic link matching.		Algoritma pengenalan populer termasuk analisis komponen utama menggunakan eigenfaces, linear discriminant analysis, elastic bunch graph matching menggunakan Fisherface algorithm, the hidden Markov model, the multilinear subspace learning using tensor representation, dan neuronal motivated dynamic link matching.


	===3-~~Dimensional recognition~~===		===Pengenalan 3-Dimensi===

	Three-dimensional face recognition technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.		Three-dimensional face recognition technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.
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	A different form of taking input data for face recognition is by using thermal cameras, by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or make up. A problem with using thermal pictures for face recognition is that the databases for face recognition is limited. Diego Socolinsky, and Andrea Selinger (2004) research the use of thermal face recognition in real life, and operation sceneries, and at the same time build a new database of thermal face images. The research uses low-sensitive, low-resolution ferro-electric electrics sensors that are capable of acquire long wave thermal infrared (LWIR). The results show that a fusion of LWIR and regular visual cameras has the greater results in outdoor probes. Indoor results show that visual has a 97.05% accuracy, while LWIR has 93.93%, and the Fusion has 98.40%, however on the outdoor proves visual has 67.06%, LWIR 83.03%, and fusion has 89.02%. The study used 240 subjects over the period of 10 weeks to create the new database. The data was collected on sunny, rainy, and cloudy days.		A different form of taking input data for face recognition is by using thermal cameras, by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or make up. A problem with using thermal pictures for face recognition is that the databases for face recognition is limited. Diego Socolinsky, and Andrea Selinger (2004) research the use of thermal face recognition in real life, and operation sceneries, and at the same time build a new database of thermal face images. The research uses low-sensitive, low-resolution ferro-electric electrics sensors that are capable of acquire long wave thermal infrared (LWIR). The results show that a fusion of LWIR and regular visual cameras has the greater results in outdoor probes. Indoor results show that visual has a 97.05% accuracy, while LWIR has 93.93%, and the Fusion has 98.40%, however on the outdoor proves visual has 67.06%, LWIR 83.03%, and fusion has 89.02%. The study used 240 subjects over the period of 10 weeks to create the new database. The data was collected on sunny, rainy, and cloudy days.



	==Pranala Menarik==		==Pranala Menarik==

Onnowpurbo: /* OpenBiometrics */

2018-05-23T02:21:33Z

OpenBiometrics

← Older revision		Revision as of 02:21, 23 May 2018
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	* [[OpenBiometrics]]		* [[OpenBiometrics]]
			* [[OpenBR: Instalasi]]

Onnowpurbo: /* OpenFace */

2018-05-22T20:38:39Z

OpenFace

← Older revision		Revision as of 20:38, 22 May 2018
Line 62:		Line 62:
	* [[Openface: Instalasi di Ubuntu 16.04 Server]]		* [[Openface: Instalasi di Ubuntu 16.04 Server]]
	* [[Openface: Instalasi di Ubuntu 18.04 Desktop]]		* [[Openface: Instalasi di Ubuntu 18.04 Desktop]]
			* [[Openface: Instalasi Deep Learning di Ubuntu 16.04 Server]]
	* [[Openface: contoh face detection system]]		* [[Openface: contoh face detection system]]

Onnowpurbo: /* OpenFace */

2018-05-22T03:24:01Z

OpenFace

← Older revision		Revision as of 03:24, 22 May 2018
Line 62:		Line 62:
	* [[Openface: Instalasi di Ubuntu 16.04 Server]]		* [[Openface: Instalasi di Ubuntu 16.04 Server]]
	* [[Openface: Instalasi di Ubuntu 18.04 Desktop]]		* [[Openface: Instalasi di Ubuntu 18.04 Desktop]]
			* [[Openface: contoh face detection system]]

	===OpenBiometrics===		===OpenBiometrics===

	* [[OpenBiometrics]]		* [[OpenBiometrics]]