PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS YUDHARTA PASURUAN

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1 PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS YUDHARTA PASURUAN
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 Pengenalan Sidik Jari Pra Pemrosesan Ekstraksi Pola Tangan
Nilai Ambang

12 Komponen Sistem Pengenalan Sidik Jari
Alat optik untuk mengambil gambar sidik jari Komputer untuk pemrosesan gambar Perangkat lunak Penyimpanan data multimedia (gambar sidik jari) Jaringan komputer Integrasi sistem 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 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 Pencarian Wajah

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