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PENGENALAN POLA

PENGENALAN POLA. PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS YUDHARTA PASURUAN. Pattern Recognition. A basic attribute of people – categorisation of sensory input. Examples of Pattern Recognition tasks ‘Reading’ facial expressions Recognising Speech Reading a Document

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PENGENALAN POLA

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  1. PENGENALAN POLA PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS YUDHARTA PASURUAN

  2. Pattern Recognition A basic attribute of people – categorisation of sensory input • Examples of Pattern Recognition tasks • ‘Reading’ facial expressions • Recognising Speech • Reading a Document • Identifying a person by fingerprints • Diagnosis from medical images • Wine tasting

  3. Machine Recognition of Patterns • Feature extractor makes some measurements on the input pattern. • X is called Feature Vector. Often, X ∈ R • Classifier maps each feature vector to a class label. • Features to be used are problem-specific.

  4. Some Examples of PR Tasks • Character Recognition • Pattern – Image. • Class – identity of character • Features: Binary image, projections (e.g., row and column sums), Moments etc.

  5. Some Examples of PR Tasks • Speech Recognition • Pattern – 1-D signal (or its sampled version) • Class – identity of speech units • Features – LPC model of chunks of speech, spectral info, cepstrum etc. • Pattern can become a sequence of feature vectors.

  6. Examples contd... • Fingerprint based identity verification • Pattern – image plus a identity claim • Class – Yes / No • Features: Position of Minutiae, Orientation field of ridge lines etc.

  7. Examples contd... • Video-based Surveillance • Pattern – video sequence • Class – e.g., level of alertness • Features – Motion trajectories, Parameters of a prefixed model etc.

  8. Examples contd... • Credit Screening • Pattern – Details of an applicant (for, e.g., credit card) • Class – Yes / No • Features: income, job history, level of credit, credit history etc.

  9. Examples contd... • Imposter detection (of, e.g., credit card) • Pattern – A sequence of transactions • Class – Yes / No • Features: Amount of money, locations of transactions, times between transactions etc.

  10. Examples contd... • Document Classification • Pattern – A document and a querry • Class – Relevant or not (in general, rank) • Features – word occurrence counts, word context etc. • Spam filtering, diagnostics of machinery etc.

  11. PengenalanSidikJari PraPemrosesan Ekstraksi Pola Tangan Nilai Ambang

  12. KomponenSistemPengenalanSidikJari • Alatoptikuntukmengambilgambarsidikjari • Komputeruntukpemrosesangambar • Perangkatlunak • Penyimpanan data multimedia (gambarsidikjari) • Jaringankomputer • Integrasisistem DIKENALI Alat optik untuk mengambil gambar sidik jari Server Utama

  13. Pengenalan Suara

  14. Pengenalan Wajah

  15. Contoh Pola Visual • The University of Bern (UB) face database memuat 30 orang yang masing-masing memiliki 10 citra wajah frontal. • Basisdata UB memiliki karakteristik adanya perubahan yang relatif kecil pada facial expression serta perubahan posisi kepala kearah kiri, kanan, atas dan bawah sebesar 30 derajat

  16. Pencarian Wajah Deteksi Wajah Tampak Muka • Deteksi muka tampak depan dan bagian vertikalnya: •  Data muka tersedia dengan semua ukuran dan skala tertentu •  Data muka setiap orang tersedia dengan beberapa ekspresi berbeda •  Kondisi pencahayaan mempengaruhi pengenalan •  Ciri khusus pada gambar wajah (tua/muda, laki-laki/perempuan, memakai kacamata) • Tujuan desain sistem deteksi wajah: • Membuat algoritma yang cepat • Membuat algoritma dengan keakurasian tinggi

  17. Komponen Sistem Pengenalan Wajah • Alat optik untuk mengambil gambar wajah • Komputer untuk pemrosesan gambar • Perangkat lunak • Penyimpanan data multimedia (gambar wajah) • Jaringan komputer • Integrasi sistem Server Utama DITOLAK

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