A fingerprint sensor, comprising a sensing part to be touched by a finger, the sensing part comprising a plurality of sensing units, wherein the fingerprint sensor further comprises a processing unit, a correction unit and a storage unit, wherein: the plurality of sensing units are configured such that, after the fingerprint sensor is switched to a correction mode, each of the plurality of sensing units is pressed by a fingerprint ridge part to sense a fingerprint signal and obtain a frame of first data (y), before the processing unit performs correction; the processing unit is configured to calculate, according to a mathematical model, new correction coefficients of each corresponding sensing unit by using the first data (y) of the each corresponding sensing unit; and the correction unit is configured to correct, according to the new correction coefficients of each corresponding sensing unit, and based on a liner model of x 1,1... x 1, M......... x N, 1... x N, M = y 1,1 ′ − b 1,1 k 1,1... y 1, M ′ − b 1, M k 1, M......... y N, 1 ′ − b N, 1 k N, 1 ​... y N, M ′ − b N, M k N, M, each second data output by the corresponding sensing unit after correction; wherein N and M represent rows and columns of the sensing units in the fingerprint sensor, respectively; y' represents second data output by each corresponding sensing unit, and x represents data after correction process by means of the correction coefficients of each corresponding sensing unit; the storage unit is configured to store correction coefficients of the plurality of sensing units; characterized in that, the processing unit comprises a first calculation subunit, configured to calculate, according to the mathematical model, the new correction coefficients of each corresponding sensing unit by using the first data and old correction coefficients of each corresponding sensing unit, k new = 1 − σ k old + σ y F Y b = 1 − σ b old + σ y − Avg Y wherein k and b represent the new correction coefficients of each sensing unit; k old and b old represent the old correction coefficients of each sensing unit; y represent the first data output by each sensing unit; Y represents an image constituted by all first data output by all the sensing units; F represents data of each corresponding sensing unit after performing median filtering on Y; Avg represents data of each corresponding sensing unit after calculating an average of Y; σ represents a learning factor, wherein a range of σ is [0,1] and σ = x old − F Y x old + F Y; and x old represents data of each sensing unit obtained after correction process by means of the old correction coefficients of each sensing unit. the plurality of sensing units are configured such that, after the fingerprint sensor is switched to a correction mode, each of the plurality of sensing units is pressed by a fingerprint ridge part to sense a fingerprint signal and obtain a frame of first data (y), before the processing unit performs correction; the processing unit is configured to calculate, according to a mathematical model, new correction coefficients of each corresponding sensing unit by using the first data (y) of the each corresponding sensing unit; and the correction unit is configured to correct, according to the new correction coefficients of each corresponding sensing unit, and based on a liner model of x 1,1... x 1, M......... x N, 1... x N, M = y 1,1 ′ − b 1,1 k 1,1... y 1, M ′ − b 1, M k 1, M......... y N, 1 ′ − b N, 1 k N, 1 ​... y N, M ′ − b N, M k N, M, each second data output by the corresponding sensing unit after correction; wherein N and M represent rows and columns of the sensing units in the fingerprint sensor, respectively; y' represents second data output by each corresponding sensing unit, and x represents data after correction process by means of the correction coefficients of each corresponding sensing unit; the storage unit is configured to store correction coefficients of the plurality of sensing units; characterized in that, the processing unit comprises a first calculation subunit, configured to calculate, according to the mathematical model, the new correction coefficients of each corresponding sensing unit by using the first data and old correction coefficients of each corresponding sensing unit, k new = 1 − σ k old + σ y F Y b = 1 − σ b old + σ y − Avg Y wherein k and b represent the new correction coefficients of each sensing unit; k old and b old represent the old correction coefficients of each sensing unit; y represent the first data output by each sensing unit; Y represents an image constituted by all first data output by all the sensing units; F represents data of each corresponding sensing unit after performing median filtering on Y; Avg represents data of each corresponding sensing unit after calculating an average of Y; σ represents a learning factor, wherein a range of σ is [0,1] and σ = x old − F Y x old + F Y; and x old represents data of each sensing unit obtained after correction process by means of the old correction coefficients of each sensing unit.