Classification of foot images according to person, age, and gender with the local phase quantization

Öz

Foot images are an important biological feature of the human body and carry various characteristics of people. The texture in the footprint, shape, length, etc. It may be possible to identify a person by looking at different qualities. Although the hand structure has its own unique shape and skin texture, comparison of these with foot biometrics reveals a complex situation. The main reasons for this include close toes, the absence of typical lines in footprints, and the high noise content of turned footprints. However, although these details are not similar to the hand, they cause differences in foot images for each person. In addition to these, foot images also differ according to age, gender, race, shoes and age of starting to wear shoes. In this study, 6944 data, which are right and left foot images of 100 people, were collected. The features of these collected files were extracted by local phase quantization. For each image file, 1x256 vectors were produced. These processes were performed for all files and images were classified for person, age and gender with many different classification algorithms. While 99.42% accuracy rate was obtained for person recognition, 99.87% success was achieved for gender. Finally, 98.14% classification success was achieved for age. All these results show that recognition from foot images is possible with high success with the method here.

Anahtar Kelimeler

• Foot image
• Local phase quantization
• Classification

Yerel faz niceleme ile ayak görüntülerinin kişi, yaş ve cinsiyete göre sınıflandırılması

Öz

Ayak görüntüleri insan vücudunun önemli bir biyolojik özelliği olup insanların çeşitli özelliklerini taşır. Ayak izindeki doku, şekil, uzunluk vb. farklı niteliklere bakılarak kişi tanımlamak mümkün olabilir. El yapısı kendine özgü şekil ve cilt dokusuna sahip olsa da bunların ayak biyometrisi ile karşılaştırılması karmaşık bir durum ortaya çıkarmaktadır. Bunun temel nedenleri arasında yakın ayak parmakları, ayak izlerindeki tipik çizgilerin yokluğu ve dönmüş ayak izlerinin yüksek gürültü içermesi yer almaktadır. Fakat bu ayrıntılar el ile benzerlik göstermese de her kişi için ayak görüntülerinde farklılık oluşmasına neden olmaktadır. Bunların yanı sıra ayak görüntüleri yaş, cinsiyet, ırk, ayakkabılar ve ayakkabı giymeye başlama yaşına göre de farklılık göstermektedir. Buradaki çalışma da 100 kişiye ait sağ ve sol ayak görüntüleri olan 6944 veri toplanmıştır. Toplanan bu dosyaların yerel faz niceleme ile öznitelikleri çıkarılmıştır. Her bir görüntü dosyası için 1x256 boyutlarında vektör üretilmiştir. Tüm dosyalar için bu işlemler yapılmış ve birçok farklı sınıflandırma algoritmaları ile görüntüler kişi, yaş ve cinsiyet için sınıflandırılmıştır. Kişi tanıma için % 99.42 oranında doğruluk oranı elde edilirken, cinsiyet için % 99.87 oranında başarı elde edilmiştir. Son olarak yaş için ise % 98.14 oranında sınıflandırma başarısına ulaşılmıştır.

Anahtar Kelimeler

• ayak görüntüsü
• yerel faz niceleme
• sınıflandırma

Kaynakça

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